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Energy & Environmental Science

The role of hydrogen and fuel cells in the global energy system.

* Corresponding authors

a Centre for Environmental Policy, Imperial College London, London SW7 1NE, UK

b UCL Institute for Sustainable Resources, University College London, London WC1H 0NN, UK

c Sustainable Gas Institute, Imperial College London, UK

d Centre for Process Systems Engineering, Dept of Chemical Engineering, Imperial College London, London SW7 2AZ, UK E-mail: [email protected]

Hydrogen technologies have experienced cycles of excessive expectations followed by disillusion. Nonetheless, a growing body of evidence suggests these technologies form an attractive option for the deep decarbonisation of global energy systems, and that recent improvements in their cost and performance point towards economic viability as well. This paper is a comprehensive review of the potential role that hydrogen could play in the provision of electricity, heat, industry, transport and energy storage in a low-carbon energy system, and an assessment of the status of hydrogen in being able to fulfil that potential. The picture that emerges is one of qualified promise: hydrogen is well established in certain niches such as forklift trucks, while mainstream applications are now forthcoming. Hydrogen vehicles are available commercially in several countries, and 225 000 fuel cell home heating systems have been sold. This represents a step change from the situation of only five years ago. This review shows that challenges around cost and performance remain, and considerable improvements are still required for hydrogen to become truly competitive. But such competitiveness in the medium-term future no longer seems an unrealistic prospect, which fully justifies the growing interest and policy support for these technologies around the world.

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I. Staffell, D. Scamman, A. Velazquez Abad, P. Balcombe, P. E. Dodds, P. Ekins, N. Shah and K. R. Ward, Energy Environ. Sci. , 2019,  12 , 463 DOI: 10.1039/C8EE01157E

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• Perspective
• Published: 18 October 2022

Machine learning for a sustainable energy future

• Zhenpeng Yao   ORCID: orcid.org/0000-0001-8286-8257 1 , 2 , 3 , 4   na1 ,
• Yanwei Lum   ORCID: orcid.org/0000-0001-7261-2098 5 , 6   na1 ,
• Andrew Johnston 6   na1 ,
• Luis Martin Mejia-Mendoza 2 ,
• Xin Zhou 7 ,
• Yonggang Wen 7 ,
• Alán Aspuru-Guzik   ORCID: orcid.org/0000-0002-8277-4434 2 , 8 ,
• Edward H. Sargent   ORCID: orcid.org/0000-0003-0396-6495 6 &
• Zhi Wei Seh   ORCID: orcid.org/0000-0003-0953-567X 5  

Nature Reviews Materials volume  8 ,  pages 202–215 ( 2023 ) Cite this article

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• Computer science

Electrocatalysis

• Energy grids and networks
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Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances — at the materials, devices and systems levels — for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.

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Introduction.

The combustion of fossil fuels, used to fulfill approximately 80% of the world’s energy needs, is the largest single source of rising greenhouse gas emissions and global temperature 1 . The increased use of renewable sources of energy, notably solar and wind power, is an economically viable path towards meeting the climate goals of the Paris Agreement 2 . However, the rate at which renewable energy has grown has been outpaced by ever-growing energy demand, and as a result the fraction of total energy produced by renewable sources has remained constant since 2000 (ref. 3 ). It is thus essential to accelerate the transition towards sustainable sources of energy 4 . Achieving this transition requires energy technologies, infrastructure and policies that enable and promote the harvest, storage, conversion and management of renewable energy.

In sustainable energy research, suitable material candidates (such as photovoltaic materials) must first be chosen from the combinatorial space of possible materials, then synthesized at a high enough yield and quality for use in devices (such as solar panels). The time frame of a representative materials discovery process is 15–20 years 5 , 6 , leaving considerable room for improvement. Furthermore, the devices have to be optimized for robustness and reproducibility to be incorporated into energy systems (such as in solar farms) 7 , where management of energy usage and generation patterns is needed to further guarantee commercial success.

Here we explore the extent to which machine learning (ML) techniques can help to address many of these challenges 8 , 9 , 10 . ML models can be used to predict specific properties of new materials without the need for costly characterization; they can generate new material structures with desired properties; they can understand patterns in renewable energy usage and generation; and they can help to inform energy policy by optimizing energy management at both device and grid levels.

In this Perspective, we introduce Acc(X)eleration Performance Indicators (XPIs), which can be used to measure the effectiveness of platforms developed for accelerated energy materials discovery. Next, we discuss closed-loop ML frameworks and evaluate the latest advances in applying ML to the development of energy harvesting, storage and conversion technologies, as well as the integration of ML into a smart power grid. Finally, we offer an overview of energy research areas that stand to benefit further from ML.

Performance indicators

Because many reports discuss ML-accelerated approaches for materials discovery and energy systems management, we posit that there should be a consistent baseline from which these reports can be compared. For energy systems management, performance indicators at the device, plant and grid levels have been reported 11 , 12 , yet there are no equivalent counterparts for accelerated materials discovery.

The primary goal in materials discovery is to develop efficient materials that are ready for commercialization. The commercialization of a new material requires intensive research efforts that can span up to two decades: the goal of every accelerated approach should be to accomplish commercialization an order-of-magnitude faster. The materials science field can benefit from studying the case of vaccine development. Historically, new vaccines take 10 years from conception to market 13 . However, after the start of the COVID-19 pandemic, several companies were able to develop and begin releasing vaccines in less than a year. This achievement was in part due to an unprecedented global research intensity, but also to a shift in the technology: after a technological breakthrough in 2008, the cost of sequencing DNA began decreasing exponentially 14 , 15 , enabling researchers to screen orders-of-magnitude more vaccines than was previously possible.

ML for energy technologies has much in common with ML for other fields like biomedicine, sharing the same methodology and principles. However, in practice, ML models for different technologies are exposed to additional unique requirements. For example, ML models for medical applications usually have complex structures that take into account regulatory oversight and ensure the safe development, use and monitoring of systems, which usually does not happen in the energy field 16 . Moreover, data availability varies substantially from field to field; biomedical researchers can work with a relatively large amount of data that energy researchers usually lack. This limited data accessibility can constrain the usage of sophisticated ML models (such as deep learning models) in the energy field. However, adaptation has been quick in all energy subfields, with a rapidly increased number of groups recognizing the importance of statistical methods and starting to use them for various problems. We posit that the use of high-throughput experimentation and ML in materials discovery workflows can result in breakthroughs in accelerating development, but the field first needs a set of metrics with which ML models can be evaluated and compared.

Accelerated materials discovery methods should be judged based on the time it takes for a new material to be commercialized. We recognize that this is not a useful metric for new platforms, nor is it one that can be used to decide quickly which platform is best suited for a particular scenario. We therefore propose here XPIs that new materials discovery platforms should report.

Acceleration factor of new materials, XPI-1

This XPI is evaluated by dividing the number of new materials that are synthesized and characterized per unit time with the accelerated platform by the number of materials that are synthesized and characterized with traditional methods. For example, an acceleration factor of ten means that for a given time period, the accelerated platform can evaluate ten times more materials than a traditional platform. For materials with multiple target properties, researchers should report the rate-limiting acceleration factor.

Number of new materials with threshold performance, XPI-2

This XPI tracks the number of new materials discovered with an accelerated platform that have a performance greater than the baseline value. The selection of this baseline value is critical: it should be something that fairly captures the standard to which new materials need to be compared. As an example, an accelerated platform that seeks to discover new perovskite solar cell materials should track the number of devices made with new materials that have a better performance than the best existing solar cell 17 .

Performance of best material over time, XPI-3

This XPI tracks the absolute performance — whether it is Faradaic efficiency, power conversion efficiency or other — of the best material as a function of time. For the accelerated framework, the evolution of the performance should increase faster than the performance obtained by traditional methods 18 .

Repeatability and reproducibility of new materials, XPI-4

This XPI seeks to ensure that the new materials discovered are consistent and repeatable: this is a key consideration to screen out materials that would fail at the commercialization stage. The performance of a new material should not vary by more than x % of its mean value (where x is the standard error): if it does, this material should not be included in either XPI-2 (number of new materials with threshold performance) or XPI-3 (performance of best material over time).

Human cost of the accelerated platform, XPI-5

This XPI reports the total costs of the accelerated platform. This should include the total number of researcher hours needed to design and order the components for the accelerated system, develop the programming and robotic infrastructure, develop and maintain databases used in the system and maintain and run the accelerated platform. This metric would provide researchers with a realistic estimate of the resources required to adapt an accelerated platform for their own research.

Use of the XPIs

Each of these XPIs can be measured for computational, experimental or integrated accelerated systems. Consistently reporting each of these XPIs as new accelerated platforms are developed will allow researchers to evaluate the growth of these platforms and will provide a consistent metric by which different platforms can be compared. As a demonstration, we applied the XPIs to evaluate the acceleration performance of several typical platforms: Edisonian-like trial-test, robotic photocatalysis development 19 and design of a DNA-encoded-library-based kinase inhibitor 20 (Table  1 ). To obtain a comprehensive performance estimate, we define one overall acceleration score S adhering to the following rules. The dependent acceleration factors (XPI-1 and XPI-2), which function in a synergetic way, are added together to reflect their contribution as a whole. The independent acceleration factors (XPI-3, XPI-4 and XPI-5), which may function in a reduplicated way, are multiplied together to value their contributions respectively. As a result, the overall acceleration score can be calculated as S  = (XPI-1 + XPI-2) × XPI-3 × XPI-4 ÷ XPI-5. As the reference, the Edisonian-like approach has a calculated overall XPIs score of around 1, whereas the most advanced method, the DNA-encoded-library-based drug design, exhibits an overall XPIs score of 10 7 . For the sustainability field, the robotic photocatalysis platform has an overall XPIs score of 10 5 .

For energy systems, the most frequently reported XPI is the acceleration factor, in part because it is deterministic, but also because it is easy to calculate at the end of the development of a workflow. In most cases, we expect that authors report the acceleration factor only after completing the development of the platform. Reporting the other suggested XPIs will provide researchers with a better sense of both the time and human resources required to develop the platform until it is ready for publication. Moving forward, we hope that other researchers adopt the XPIs — or other similar metrics — to allow for fair and consistent comparison between the different methods and algorithms that are used to accelerate materials discovery.

Closed-loop ML for materials discovery

The traditional approach to materials discovery is often Edisonian-like, relying on trial and error to develop materials with specific properties. First, a target application is identified, and a starting pool of possible candidates is selected (Fig.  1a ). The materials are then synthesized and incorporated into a device or system to measure their properties. These results are then used to establish empirical structure–property relationships, which guide the next round of synthesis and testing. This slow process goes through as many iterations as required and each cycle can take several years to complete.

a | Traditional Edisonian-like approach, which involves experimental trial and error. b | High-throughput screening approach involving a combination of theory and experiment. c | Machine learning (ML)-driven approach whereby theoretical and experimental results are used to train a ML model for predicting structure–property relationships. d | ML-driven approach for property-directed and automatic exploration of the chemical space using optimization ML (such as genetic algorithms or generative models) that solve the ‘inverse’ design problem.

A computation-driven, high-throughput screening strategy (Fig.  1b ) offers a faster turnaround. To explore the overall vast chemical space (~10 60 possibilities), human intuition and expertise can be used to create a library with a substantial number of materials of interest (~10 4 ). Theoretical calculations are carried out on these candidates and the top performers (~10 2 candidates) are then experimentally verified. With luck, the material with the desired functionality is ‘discovered’. Otherwise, this process is repeated in another region of the chemical space. This approach can still be very time-consuming and computationally expensive and can only sample a small region of the chemical space.

ML can substantially increase the chemical space sampled, without costing extra time and effort. ML is data-driven, screening datasets to detect patterns, which are the physical laws that govern the system. In this case, these laws correspond to materials structure–property relationships. This workflow involves high-throughput virtual screening (Fig.  1c ) and begins by selecting a larger region (~10 6 ) of the chemical space of possibilities using human intuition and expertise. Theoretical calculations are carried out on a representative subset (~10 4 candidates) and the results are used for training a discriminative ML model. The model can then be used to make predictions on the other candidates in the overall selected chemical space 9 . The top ~10 2 candidates are experimentally verified, and the results are used to improve the predictive capabilities of the model in an iterative loop. If the desired material is not ‘discovered’, the process is repeated on another region of the chemical space.

An improvement on the previous approaches is a framework that requires limited human intuition or expertise to direct the chemical space search: the automated virtual screening approach (Fig.  1d ). To begin with, a region of the chemical space is picked at random to initiate the process. Thereafter, this process is similar to the previous approach, except that the computational and experimental data is also used to train a generative learning model. This generative model solves the ‘inverse’ problem: given a required property, the goal is to predict an ideal structure and composition in the chemical space. This enables a directed, automated search of the chemical space, towards the goal of ‘discovering’ the ideal material 8 .

ML for energy

ML has so far been used to accelerate the development of materials and devices for energy harvesting (photovoltaics), storage (batteries) and conversion (electrocatalysis), as well as to optimize power grids. Besides all the examples discussed here, we summarize the essential concepts in ML (Box  1 ), the grand challenges in sustainable materials research (Box  2 ) and the details of key studies (Table  2 ).

Box 1 Essential concepts in ML

With the availability of large datasets 122 , 125 and increased computing power, various machine learning (ML) algorithms have been developed to solve diverse problems in energy. Below, we provide a brief overview of the types of problem that ML can solve in energy technology, and we then summarize the status of ML-driven energy research. More detailed information about the nuts and bolts of ML techniques can be found in previous reviews 173 , 174 , 175 .

Property prediction

Supervised learning models are predictive (or discriminative) models that are given a datapoint x , and seek to predict a property y (for example, the bandgap 27 ) after being trained on a labelled dataset. The property y can be either continuous or discrete. These models have been used to aid or even replace physical simulations or measurements under certain circumstances 176 , 177 .

Generative materials design

Unsupervised learning models are generative models that can generate or output new examples x ′ (such as new molecules 104 ) after being trained on an unlabelled dataset. This generation of new examples can be further enhanced with additional information (physical properties) to condition or bias the generative process, allowing the models to generate examples with improved properties and leading to the property-to-structure approach called inverse design 52 , 178 .

Self-driving laboratories

Self-driving or autonomous laboratories 19 use ML models to plan and perform experiments, including the automation of retrosynthesis analysis (such as in reinforcement-learning-aided synthesis planning 124 , 179 ), prediction of reaction products (such as in convolutional neural networks (CNNs) for reaction prediction 137 , 138 ) and reaction condition optimization (such as in robotic workflows optimized by active learning 19 , 160 , 180 , 181 , 182 , 183 ). Self-driving laboratories, which use active learning for iterating through rounds of synthesis and measurements, are a key component in the closed-loop inverse design 52 .

Aiding characterization

ML models have been used to aid the quantitative or qualitative analysis of experimental observations and measurements, including assisting in the determination of crystal structure from transmission electron microscopy images 184 , identifying coordination environment 81 and structural transition 83 from X-ray absorption spectroscopy and inferring crystal symmetry from electron diffraction 176 .

Accelerating theoretical computations

ML models can enable otherwise intractable simulations by reducing the computational cost (processor core amount and time) for systems with increased length and timescales 69 , 70 and providing potentials and functionals for complex interactions 68 .

Optimizing system management

ML models can aid the management of energy systems at the device or grid power level by predicting lifetimes (such as battery life 43 , 44 ), adapting to new loads (such as in long short-term memory for building load prediction 95 ) and optimizing performance (such as in reinforcement learning for smart grid control 94 ).

Box 2 Grand challenges in energy materials research

Photovoltaics.

Discover non-toxic (Pd- and Cd-free) materials with good optoelectronic properties

Identify and minimize materials defects in light-absorbing materials

Design effective recombination-layer materials for tandem solar cells

Develop materials design strategies for long-term operational stability 125

Develop (hole/electron) transport materials with high carrier mobility 125

Optimize cell structure for maximum light absorption and minimum use of active materials

Tune materials bandgaps for optimal solar-harvesting performance under complex operation conditions 21 , 22

Develop Earth-abundant cathode materials (Co-free) with high reversibility and charge capacity 4

Design electrolytes with wider electrochemical windows and high conductivity 4

Identify electrolyte systems to boost battery performance and lifetime 4

Discover new molecules for redox flow batteries with suitable voltage 4

Understand correlation between defect growth in battery materials and overall degradation process of battery components

Tune operando (dis)charging protocol for minimized capacity loss, (dis)charging rate and optimal battery life under diversified conditions 7 , 53

Design materials with optimal adsorption energy for maximized catalytic activity 60 , 61

Identify and study active sites on catalytic materials 58

Engineer catalytic materials for extended durability 58 , 60 , 61

Identify a fuller set of materials descriptors that relate to catalytic activity 60 , 61

Engineer multiple catalytic functionalities into the same material 60 , 61

Design multiscale electrode structures for optimized catalytic activity

Correlate atomistic contamination and growth of catalyst particles with electrode degradation process

Tune operando (dis)charging protocol for minimized capacity loss and optimal cell life

ML is accelerating the discovery of new optoelectronic materials and devices for photovoltaics, but major challenges are still associated with each step.

Photovoltaics materials discovery

One materials class for which ML has proved particularly effective is perovskites, because these materials have a vast chemical space from which the constituents may be chosen. Early representations of perovskite materials for ML were atomic-feature representations, in which each structure is encoded as a fixed-length vector comprised of an average of certain atomic properties of the atoms in the crystal structure 21 , 22 . A similar technique was used to predict new lead-free perovskite materials with the proper bandgap for solar cells 23 (Fig.  2a ). These representations allowed for high accuracy but did not account for any spatial relation between atoms 24 , 25 . Materials systems can also be represented as images 26 or as graphs 27 , enabling the treatment of systems with diverse number of atoms. The latter representation is particularly compelling, as perovskites, particularly organic–inorganic perovskites, have crystal structures that incorporate a varying number of atoms, and the organic molecules can vary in size.

a | Energy harvesting 23 . b | Energy storage 38 . c | Energy conversion 76 . d | Energy management 93 . ICSD, Inorganic Crystal Structure Database; ML, machine learning.

Although bandgap prediction is an important first step, this parameter alone is not sufficient to indicate a useful optoelectronic material; other parameters, including electronic defect density and stability, are equally important. Defect energies are addressable with computational methods, but the calculation of defects in structures is extremely computationally expensive, which inhibits the generation of a dataset of defect energies from which an ML model can be trained. To expedite the high-throughput calculation of defect energies, a Python toolkit has been developed 28 that will be pivotal in building a database of defect energies in semiconductors. Researchers can then use ML to predict both the formation energy of defects and the energy levels of these defects. This knowledge will ensure that the materials selected from high-throughput screening will not only have the correct bandgap but will also either be defect-tolerant or defect-resistant, finding use in commercial optoelectronic devices.

Even without access to a large dataset of experimental results, ML can accelerate the discovery of optoelectronic materials. Using a self-driving laboratory approach, the number of experiments required to optimize an organic solar cell can be reduced from 500 to just 60 (ref. 29 ). This robotic synthesis method accelerates the learning rate of the ML models and drastically reduces the cost of the chemicals needed to run the optimization.

Solar device structure and fabrication

Photovoltaic devices require optimization of layers other than the active layer to maximize performance. One component is the top transparent conductive layer, which needs to have both high optical transparency and high electronic conductivity 30 , 31 . A genetic algorithm that optimized the topology of a light-trapping structure enabled a broadband absorption efficiency of 48.1%, which represents a more than threefold increase over the Yablonovitch limit, the 4 n 2 factor (where n is the refractive index of the material) theoretical limit for light trapping in photovoltaics 32 .

A universal standard irradiance spectrum is usually used by researchers to determine optimal bandgaps for solar cell operation 33 . However, actual solar irradiance fluctuates based on factors such as the position of the Sun, atmospheric phenomena and the season. ML can reduce yearly spectral sets into a few characteristic spectra 33 , allowing for the calculation of optimal bandgaps for real-world conditions.

To optimize device fabrication, a CNN was used to predict the current–voltage characteristics of as-cut Si wafers based on their photoluminescence images 34 . Additionally, an artificial neural network was used to predict the contact resistance of metallic front contacts for Si solar cells, which is critical for the manufacturing process 35 .

Although successful, these studies appear to be limited to optimizing structures and processes that are already well established. We suggest that, in future work, ML could be used to augment simulations, such as the multiphysics models for solar cells. Design of device architecture could begin from such simulation models, coupled with ML in an iterative process to quickly optimize design and reduce computational time and cost. In addition, optimal conditions for the scaling-up of device area and fabrication processes are likely to be very different from those for laboratory-scale demonstrations. However, determining these optimal conditions could be expensive in terms of materials cost and time, owing to the need to construct much larger devices. In this regard, ML, together with the strategic design of experiments, could greatly accelerate the optimization of process conditions (such as the annealing temperatures and solvent choice).

Electrochemical energy storage

Electrochemical energy storage is an essential component in applications such as electric vehicles, consumer electronics and stationary power stations. State-of-the-art electrochemical energy storage solutions have varying efficacy in different applications: for example, lithium-ion batteries exhibit excellent energy density and are widely used in electronics and electric vehicles, whereas redox flow batteries have drawn substantial attention for use in stationary power storage. ML approaches have been widely employed in the field of batteries, including for the discovery of new materials such as solid-state ion conductors 36 , 37 , 38 (Fig.  2b ) and redox active electrolytes for redox flow batteries 39 . ML has also aided battery management, for example, through state-of-charge determination 40 , state-of-health evaluation 41 , 42 and remaining-life prediction 43 , 44 .

Electrode and electrolyte materials design

Layered oxide materials, such as LiCoO 2 or LiNi x Mn y Co 1- x - y O 2 , have been used extensively as cathode materials for alkali metal-ion (Li/Na/K) batteries. However, developing new Li-ion battery materials with higher operating voltages, enhanced energy densities and longer lifetimes is of paramount interest. So far, universal design principles for new battery materials remain undefined, and hence different approaches have been explored. Data from the Materials Project have been used to model the electrode voltage profile diagrams for different materials in alkali metal-ion batteries (Na and K) 45 , leading to the proposition of 5,000 different electrode materials with appropriate moderate voltages. ML was also employed to screen 12,000 candidates for solid Li-ion batteries, resulting in the discovery of ten new Li-ion conducting materials 46 , 47 .

Flow batteries consist of active materials dissolved in electrolytes that flow into a cell with electrodes that facilitate redox reactions. Organic flow batteries are of particular interest. In flow batteries, the solubility of the active material in the electrolyte and the charge/discharge stability dictate performance. ML methods have explored the chemical space to find suitable electrolytes for organic redox flow batteries 48 , 49 . Furthermore, a multi-kernel-ridge regression method accelerated the discovery of active organic molecules using multiple feature training 48 . This method also helped in predicting the solubility dependence of anthraquinone molecules with different numbers and combinations of sulfonic and hydroxyl groups on pH. Future opportunities lie in the exploration of large combinatorial spaces for the inverse design of high-entropy electrodes 50 and high-voltage electrolytes 51 . To this end, deep generative models can assist the discovery of new materials based on the simplified molecular input line entry system (SMILES) representation of molecules 52 .

Battery device and stack management

A combination of mechanistic and semi-empirical models is currently used to estimate capacity and power loss in lithium-ion batteries. However, the models are applicable only to specific failure mechanisms or situations and cannot predict the lifetimes of batteries at the early stages of usage. By contrast, mechanism-agnostic models based on ML can accurately predict battery cycle life, even at an early stage of a battery’s life 43 . A combined early-prediction and Bayesian optimization model has been used to rapidly identify the optimal charging protocol with the longest cycle life 44 . ML can be used to accelerate the optimization of lithium-ion batteries for longer lifetimes 53 , but it remains to be seen whether these models can be generalized to different battery chemistries 54 .

ML methods can also predict important properties of battery storage facilities. A neural network was used to predict the charge/discharge profiles in two types of stationary battery systems, lithium iron phosphate and vanadium redox flow batteries 55 . Battery power management techniques must also consider the uncertainty and variability that arise from both the environment and the application. An iterative Q -learning ( reinforcement learning ) method was also designed for battery management and control in smart residential environments 56 . Given the residential load and the real-time electricity rate, the method is effective at optimizing battery charging/discharging/idle cycles. Discriminative neural network-based models can also optimize battery usage in electric vehicles 57 .

Although ML is able to predict the lifetime of batteries, the underlying degradation mechanisms are difficult to identify and correlate to the state of health and lifetime. To this end, incorporation of domain knowledge into a hybrid physics-based ML model can provide insight and reduce overfitting 53 . However, incorporating the physics of battery degradation processes into a hybrid model remains challenging; representation of electrode materials that encode both compositional and structural information is far from trivial. Validation of these models also requires the development of operando characterization techniques, such as liquid-phase transmission electron microscopy and ambient-pressure X-ray absorption spectroscopy (XAS), that reflect true operating conditions as closely as possible 54 . Ideally, these characterization techniques should be carried out in a high-throughput manner, using automated sample changers, for example, in order to generate large datasets for ML.

Electrocatalysts

Electrocatalysis enables the conversion of simple feedstocks (such as water, carbon dioxide and nitrogen) into valuable chemicals and/or fuels (such as hydrogen, hydrocarbons and ammonia), using renewable energy as an input 58 . The reverse reactions are also possible in a fuel cell, and hydrogen can be consumed to produce electricity 59 . Active and selective electrocatalysts must be developed to improve the efficiency of these reactions 60 , 61 . ML has been used to accelerate electrocatalyst development and device optimization.

Electrocatalyst materials discovery

The most common descriptor of catalytic activity is the adsorption energy of intermediates on a catalyst 61 , 62 . Although these adsorption energies can be calculated using density functional theory (DFT), catalysts possess multiple surface binding sites, each with different adsorption energies 63 . The number of possible sites increases dramatically if alloys are considered, and thus becomes intractable with conventional means 64 .

DFT calculations are critical for the search of electrocatalytic materials 65 and efforts have been made to accelerate the calculations and to reduce their computational cost by using surrogate ML models 66 , 67 , 68 , 69 . Complex reaction mechanisms involving hundreds of possible species and intermediates can also be simplified using ML, with a surrogate model predicting the most important reaction steps and deducing the most likely reaction pathways 70 . ML can also be used to screen for active sites across a random, disordered nanoparticle surface 71 , 72 . DFT calculations are performed on only a few representative sites, which are then used to train a neural network to predict the adsorption energies of all active sites.

Catalyst development can benefit from high-throughput systems for catalyst synthesis and performance evaluation 73 , 74 . An automatic ML-driven framework was developed to screen a large intermetallic chemical space for CO 2 reduction and H 2 evolution 75 . The model predicted the adsorption energy of new intermetallic systems and DFT was automatically performed on the most promising candidates to verify the predictions. This process went on iteratively in a closed feedback loop. 131 intermetallic surfaces across 54 alloys were ultimately identified as promising candidates for CO 2 reduction. Experimental validation 76 with Cu–Al catalysts yielded an unprecedented Faradaic efficiency of 80% towards ethylene at a high current density of 400 mA cm – 2 (Fig.  2c ).

Because of the large number of properties that electrocatalysts may possess (such as shape, size and composition), it is difficult to do data mining on the literature 77 . Electrocatalyst structures are complex and difficult to characterize completely; as a result, many properties may not be fully characterized by research groups in their publications. To avoid situations in which potentially promising compositions perform poorly as a result of non-ideal synthesis or testing conditions, other factors (such as current density, particle size and pH value) that affect the electrocatalyst performance must be kept consistent. New approaches such as carbothermal shock synthesis 78 , 79 may be a promising avenue, owing to its propensity to generate uniformly sized and shaped alloy nanoparticles, regardless of composition.

XAS is a powerful technique, especially for in situ measurements, and has been widely employed to gain crucial insight into the nature of active sites and changes in the electrocatalyst over time 80 . Because the data analysis relies heavily on human experience and expertise, there has been interest in developing ML tools for interpreting XAS data 81 . Improved random forest models can predict the Bader charge (a good approximation of the total electronic charge of an atom) and nearest-neighbour distances, crucial factors that influence the catalytic properties of the material 82 . The extended X-ray absorption fine structure (EXAFS) region of XAS spectra is known to contain information on bonding environments and coordination numbers. Neural networks can be used to automatically interpret EXAFS data 83 , permitting the identification of the structure of bimetallic nanoparticles using experimental XAS data, for example 84 . Raman and infrared spectroscopy are also important tools for the mechanistic understanding of electrocatalysis. Together with explainable artificial intelligence (AI), which can relate the results to underlying physics, these analyses could be used to discover descriptors hidden in spectra that could lead to new breakthroughs in electrocatalyst discovery and optimization.

Fuel cell and electrolyser device management

A fuel cell is an electrochemical device that can be used to convert the chemical energy of a fuel (such as hydrogen) into electrical energy. An electrolyser transforms electrical energy into chemical energy (such as in water splitting to generate hydrogen). ML has been used to optimize and manage their performance, predict degradation and device lifetime as well as detect and diagnose faults. Using a hybrid method consisting of an extreme learning machine, genetic algorithms and wavelet analysis, the degradation in proton-exchange membrane fuel cells has been predicted 85 , 86 . Electrochemical impedance measurements used as input for an artificial neural network have enabled fault detection and isolation in a high-temperature stack of proton-exchange membrane fuel cells 87 , 88 .

ML approaches can also be employed to diagnose faults, such as fuel and air leakage issues, in solid oxide fuel cell stacks. Artificial neural networks can predict the performance of solid oxide fuel cells under different operating conditions 89 . In addition, ML has been applied to optimize the performance of solid oxide electrolysers, for CO 2 /H 2 O reduction 90 , and chloralkali electrolysers 91 .

In the future, the use of ML for fuel cells could be combined with multiscale modelling to improve their design, for example to minimize Ohmic losses and optimize catalyst loading. For practical applications, fuel cells may be subject to fluctuations in energy output requirements (for example, when used in vehicles). ML models could be used to determine the effects of such fluctuations on the long-term durability and performance of fuel cells, similar to what has been done for predicting the state of health and lifetime for batteries. Furthermore, it remains to be seen whether the ML techniques for fuel cells can be easily generalized to electrolysers and vice versa, using transfer learning for example, given that they are essentially reactions in reverse.

Smart power grids

A power grid is responsible for delivering electrical energy from producers (such as power plants and solar farms) to consumers (such as homes and offices). However, energy fluctuations from intermittent renewable energy generators can render the grid vulnerable 92 . ML algorithms can be used to optimize the automatic generation control of power grids, which controls the power output of multiple generators in an energy system. For example, when a relaxed deep learning model was used as a unified timescale controller for the automatic generation control unit, the total operational cost was reduced by up to 80% compared with traditional heuristic control strategies 93 (Fig.  2d ). A smart generation control strategy based on multi-agent reinforcement learning was found to improve the control performance by around 10% compared with other ML algorithms 94 .

Accurate demand and load prediction can support decision-making operations in energy systems for proper load scheduling and power allocation. Multiple ML methods have been proposed to precisely predict the demand load: for example, long short-term memory was used to successfully and accurately predict hourly building load 95 . Short-term load forecasting of diverse customers (such as retail businesses) using a deep neural network and cross-building energy demand forecasting using a deep belief network have also been demonstrated effectively 96 , 97 .

Demand-side management consists of a set of mechanisms that shape consumer electricity consumption by dynamically adjusting the price of electricity. These include reducing (peak shaving), increasing (load growth) and rescheduling (load shifting) the energy demand, which allows for flexible balancing of renewable electricity generation and load 98 . A reinforcement-learning-based algorithm resulted in substantial cost reduction for both the service provider and customer 99 . A decentralized learning-based residential demand scheduling technique successfully shifted up to 35% of the energy demand to periods of high wind availability, substantially saving power costs compared with the unscheduled energy demand scenario 100 . Load forecasting using a multi-agent approach integrates load prediction with reinforcement learning algorithms to shift energy usage (for example, to different electrical devices in a household) for its optimization 101 . This approach reduced peak usage by more than 30% and increased off-peak usage by 50%, reducing the cost and energy losses associated with energy storage.

Opportunities for ML in renewable energy

ML provides the opportunity to enable substantial further advances in different areas of the energy materials field, which share similar materials-related challenges (Fig.  3 ). There are also grand challenges for ML application in smart grid and policy optimization.

a | Energy materials present additional modelling challenges. Machine learning (ML) could help in the representation of structurally complex structures, which can include disordering, dislocations and amorphous phases. b | Flexible models that scale efficiently with varied dataset sizes are in demand, and ML could help to develop robust predictive models. The yellow dots stand for the addition of unreliable datasets that could harm the prediction accuracy of the ML model. c | Synthesis route prediction remains to be solved for the design of a novel material. In the ternary phase diagram, the dots stand for the stable compounds in that corresponding phase space and the red dot for the targeted compound. Two possible synthesis pathways are compared for a single compound. The score obtained would reflect the complexity, cost and so on of one synthesis pathway. d | ML-aided phase degradation prediction could boost the development of materials with enhanced cyclability. The shaded region represents the rocksalt phase, which grows inside the layered phase. The arrow marks the growth direction. e | The use of ML models could help in optimizing energy generation and energy consumption. Automating the decision-making processes associated with dynamic power supplies using ML will make the power distribution more efficient. f | Energy policy is the manner in which an entity (for example, a government) addresses its energy issues, including conversion, distribution and utilization, where ML could be used to optimize the corresponding economy.

Materials with novel geometries

A ML representation is effective when it captures the inherent properties of the system (such as its physical symmetries) and can be utilized in downstream ancillary tasks, such as transfer learning to new predictive tasks, building new knowledge using visualization or attribution and generating similar data distributions with generative models 102 .

For materials, the inputs are molecules or crystal structures whose physical properties are modelled by the Schrödinger equation. Designing a general representation of materials that reflects these properties is an ongoing research problem. For molecular systems, several representations have been used successfully, including fingerprints 103 , SMILES 104 , self-referencing embedded strings (SELFIES) 105 and graphs 106 , 107 , 108 . Representing crystalline materials has the added complexity of needing to incorporate periodicity in the representation. Methods like the smooth overlap of atomic positions 109 , Voronoi tessellation 110 , 111 , diffraction images 112 , multi-perspective fingerprints 113 and graph-based algorithms 27 , 114 have been suggested, but typically lack the capability for structure reconstruction.

Complex structural systems found in energy materials present additional modelling challenges (Fig.  3a ): a large number of atoms (such as in reticular frameworks or polymers), specific symmetries (such as in molecules with a particular space group and for reticular frameworks belonging to a certain topology), atomic disordering, partial occupancy, or amorphous phases (leading to an enormous combinatorial space), defects and dislocations (such as interfaces and grain boundaries) and low-dimensionality materials (as in nanoparticles). Reduction approximations alleviate the first issue (using, for example, RFcode for reticular framework representation) 8 , but the remaining several problems warrant intensive future research efforts.

Self- supervised learning , which seeks to lever large amounts of synthetic labels and tasks to continue learning without experimental labels 115 , multi-task learning 116 , in which multiple material properties can be modelled jointly to exploit correlation structure between properties, and meta-learning 117 , which looks at strategies that allow models to perform better in new datasets or in out-of-distribution data, all offer avenues to build better representations. On the modelling front, new advances in attention mechanisms 118 , 119 , graph neural networks 120 and equivariant neural networks 121 expand our range of tools with which to model interactions and expected symmetries.

Robust predictive models

Predictive models are the first step when building a pipeline that seeks materials with desired properties. A key component for building these models is training data; more data will often translate into better-performing models, which in turn will translate into better accuracy in the prediction of new materials. Deep learning models tend to scale more favourably with dataset size than traditional ML approaches (such as random forests). Dataset quality is also essential. However, experiments are usually conducted under diverse conditions with large variation in untracked variables (Fig.  3b ). Additionally, public datasets are more likely to suffer from publication bias, because negative results are less likely to be published even though they are just as important as positive results when training statistical models 122 .

Addressing these issues require transparency and standardization of the experimental data reported in the literature. Text and natural language processing strategies could then be employed to extract data from the literature 77 . Data should be reported with the belief that it will eventually be consolidated in a database, such as the MatD3 database 123 . Autonomous laboratory techniques will help to address this issue 19 , 124 . Structured property databases such as the Materials Project 122 and the Harvard Clean Energy Project 125 can also provide a large amount of data. Additionally, different energy fields — energy storage, harvesting and conversion — should converge upon a standard and uniform way to report data. This standard should be continuously updated; as researchers continue to learn about the systems they are studying, conditions that were previously thought to be unimportant will become relevant.

New modelling approaches that work in low-data regimes, such as data-efficient models, dataset-building strategies (active sampling) 126 and data-augmentation techniques, are also important 127 . Uncertainty quantification , data efficiency, interpretability and regularization are important considerations that improve the robustness of ML models. These considerations relate to the notion of generalizability: predictions should generalize to a new class of materials that is out of the distribution of the original dataset. Researchers can attempt to model how far away new data points are from the training set 128 or the variability in predicted labels with uncertainty quantification 129 . Neural networks are a flexible model class, and often models can be underspecified 130 . Incorporating regularization, inductive biases or priors can boost the credibility of a model. Another way to create trustable models could be to enhance the interpretability of ML algorithms by deriving feature relevance and scoring their importance 131 . This strategy could help to identify potential chemically meaningful features and form a starting point for understanding latent factors that dominate material properties. These techniques can also identify the presence of model bias and overfitting, as well as improving generalization and performance 132 , 133 , 134 .

Stable and synthesizable new materials

The formation energy of a compound is used to estimate its stability and synthesizability 135 , 136 . Although negative values usually correspond to stable or synthesizable compounds, slightly positive formation energies below a limit lead to metastable phases with unclear synthesizability 137 , 138 . This is more apparent when investigating unexplored chemical spaces with undetermined equilibrium ground states; yet often the metastable phases exhibit superior properties, as seen in photovoltaics 136 , 139 and ion conductors 140 , for example. It is thus of interest to develop a method to evaluate the synthesizability of metastable phases (Fig.  3c ). Instead of estimating the probability that a particular phase can be synthesized, one can instead evaluate its synthetic complexity using ML. In organic chemistry, synthesis complexity is evaluated according to the accessibility of the phases’ synthesis route 141 or precedent reaction knowledge 142 . Similar methodologies can be applied to the inorganic field with the ongoing design of automated synthesis-planning algorithms for inorganic materials 143 , 144 .

Synthesis and evaluation of a new material alone does not ensure that material will make it to market; material stability is a crucial property that takes a long time to evaluate. Degradation is a generally complex process that occurs through the loss of active matter or growth of inactive phases (such as the rocksalt phases formed in layered Li-ion battery electrodes 145 (Fig.  3d ) or the Pt particle agglomeration in fuel cells 146 ) and/or propagation of defects (such as cracks in cycled battery electrode 147 ). Microscopies such as electron microscopy 148 and simulations such as continuum mechanics modelling 149 are often used to investigate growth and propagation dynamics (that is, phase boundary and defect surface movements versus time). However, these techniques are usually expensive and do not allow rapid degradation prediction. Deep learning techniques such as convolutional neural networks and recurrent neural networks may be able to predict the phase boundary and/or defect pattern evolution under certain conditions after proper training 150 . Similar models can then be built to understand multiple degradation phenomena and aid the design of materials with improved cycle life.

Optimized smart power grids

A promising prospect of ML in smart grids is automating the decision-making processes that are associated with dynamic power supplies to distribute power most efficiently (Fig.  3e ). Practical deployment of ML technologies into physical systems remains difficult because of data scarcity and the risk-averse mindset of policymakers. The collection of and access to large amounts of diverse data is challenging owing to high cost, long delays and concerns over compliance and security 151 . For instance, to capture the variation of renewable resources owing to peak or off-peak and seasonal attributes, long-term data collections are implemented for periods of 24 hours to several years 152 . Furthermore, although ML algorithms are ideally supposed to account for all uncertainties and unpredictable situations in energy systems, the risk-adverse mindset in the energy management industry means that implementation still relies on human decision-making 153 .

An ML-based framework that involves a digital twin of the physical system can address these problems 154 , 155 . The digital twin represents the digitalized cyber models of the physical system and can be constructed from physical laws and/or ML models trained using data sampled from the physical system. This approach aims to accurately simulate the dynamics of the physical system, enabling relatively fast generation of large amounts of high-quality synthetic data at low cost. Notably, because ML model training and validation is performed on the digital twin, there is no risk to the actual physical system. Based on the prediction results, suitable actions can be suggested and then implemented in the physical system to ensure stability and/or improve system operation.

Policy optimization

Finally, research is generally focused on one narrow aspect of a larger problem; we argue that energy research needs a more integrated approach 156 (Fig.  3f ). Energy policy is the manner in which an entity, such as the government, addresses its energy issues, including conversion, distribution and utilization. ML has been used in the fields of energy economics finance for performance diagnostics (such as for oil wells), energy generation (such as wind power) and consumption (such as power load) forecasts and system lifespan (such as battery cell life) and failure (such as grid outage) prediction 157 . They have also been used for energy policy analysis and evaluation (for example, for estimating energy savings). A natural extension of ML models is to use them for policy optimization 158 , 159 , a concept that has not yet seen widespread use. We posit that the best energy policies — including the deployment of the newly discovered materials — can be improved and augmented with ML and should be discussed in research reporting accelerated energy technology platforms.

Conclusions

To summarize, ML has the potential to enable breakthroughs in the development and deployment of sustainable energy techniques. There have been remarkable achievements in many areas of energy technology, from materials design and device management to system deployment. ML is particularly well suited to discovering new materials, and researchers in the field are expecting ML to bring up new materials that may revolutionize the energy industry. The field is still nascent, but there is conclusive evidence that ML is at least able to expose the same trends that human researchers have noticed over decades of research. The ML field itself is still seeing rapid development, with new methodologies being reported daily. It will take time to develop and adopt these methodologies to solve specific problems in materials science. We believe that for ML to truly accelerate the deployment of sustainable energy, it should be deployed as a tool, similar to a synthesis procedure, characterization equipment or control apparatus. Researchers using ML to accelerate energy technology discovery should judge the success of the method primarily on the advances it enables. To this end, we have proposed the XPIs and some areas in which we hope to see ML deployed.

Davidson, D. J. Exnovating for a renewable energy transition. Nat. Energy 4 , 254–256 (2019).

Article   Google Scholar  

Horowitz, C. A. Paris agreement. Int. Leg. Mater. 55 , 740–755 (2016).

International Energy Agency 2018 World Energy Outlook: Executive Summary https://www.iea.org/reports/world-energy-outlook-2018 (OECD/IEA, 2018).

Chu, S., Cui, Y. & Liu, N. The path towards sustainable energy. Nat. Mater. 16 , 16–22 (2017).

Maine, E. & Garnsey, E. Commercializing generic technology: the case of advanced materials ventures. Res. Policy 35 , 375–393 (2006).

De Luna, P., Wei, J., Bengio, Y., Aspuru-Guzik, A. & Sargent, E. Use machine learning to find energy materials. Nature 552 , 23–27 (2017).

Wang, H., Lei, Z., Zhang, X., Zhou, B. & Peng, J. A review of deep learning for renewable energy forecasting. Energy Convers. Manag. 198 , 111799–111814 (2019).

Yao, Z. et al. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat. Mach. Intell. 3 , 76–86 (2021).

Rosen, A. S. et al. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter 4 , 1578–1597 (2021).

Article   CAS   Google Scholar  

Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349 , 255–260 (2015).

Personal, E., Guerrero, J. I., Garcia, A., Peña, M. & Leon, C. Key performance indicators: a useful tool to assess Smart Grid goals. Energy 76 , 976–988 (2014).

Helmus, J. & den Hoed, R. Key performance indicators of charging infrastructure. World Electr. Veh. J. 8 , 733–741 (2016).

Struck, M.-M. Vaccine R&D success rates and development times. Nat. Biotechnol. 14 , 591–593 (1996).

Moore, G. E. Cramming more components onto integrated circuits. Electronics 38 , 114–116 (1965).

Google Scholar  

Wetterstrand, K. A. DNA sequencing costs: data. NHGRI Genome Sequencing Program (GSP) www.genome.gov/sequencingcostsdata (2020).

Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380 , 1347–1358 (2019).

Jeong, J. et al. Pseudo-halide anion engineering for α-FAPbI3 perovskite solar cells. Nature 592 , 381–385 (2021).

NREL. Best research-cell efficiency chart. NREL https://www.nrel.gov/pv/cell-efficiency.html (2021).

Burger, B. et al. A mobile robotic chemist. Nature 583 , 237–241 (2020).

Clark, M. A. et al. Design, synthesis and selection of DNA-encoded small-molecule libraries. Nat. Chem. Biol. 5 , 647–654 (2009).

Pilania, G., Gubernatis, J. E. & Lookman, T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput. Mater. Sci. 129 , 156–163 (2017).

Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6 , 19375 (2016).

Lu, S. et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nat. Commun. 9 , 3405 (2018).

Askerka, M. et al. Learning-in-templates enables accelerated discovery and synthesis of new stable double perovskites. J. Am. Chem. Soc. 141 , 3682–3690 (2019).

Jain, A. & Bligaard, T. Atomic-position independent descriptor for machine learning of material properties. Phys. Rev. B 98 , 214112 (2018).

Choubisa, H. et al. Crystal site feature embedding enables exploration of large chemical spaces. Matter 3 , 433–448 (2020).

Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120 , 145301–145306 (2018).

Broberg, D. et al. PyCDT: a Python toolkit for modeling point defects in semiconductors and insulators. Comput. Phys. Commun. 226 , 165–179 (2018).

Roch, L. M. et al. ChemOS: an orchestration software to democratize autonomous discovery. PLoS ONE 15 , 1–18 (2020).

Wei, L., Xu, X., Gurudayal, Bullock, J. & Ager, J. W. Machine learning optimization of p-type transparent conducting films. Chem. Mater. 31 , 7340–7350 (2019).

Schubert, M. F. et al. Design of multilayer antireflection coatings made from co-sputtered and low-refractive-index materials by genetic algorithm. Opt. Express 16 , 5290–5298 (2008).

Wang, C., Yu, S., Chen, W. & Sun, C. Highly efficient light-trapping structure design inspired by natural evolution. Sci. Rep. 3 , 1025 (2013).

Ripalda, J. M., Buencuerpo, J. & García, I. Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations. Nat. Commun. 9 , 5126 (2018).

Demant, M., Virtue, P., Kovvali, A., Yu, S. X. & Rein, S. Learning quality rating of As-Cut mc-Si wafers via convolutional regression networks. IEEE J. Photovolt. 9 , 1064–1072 (2019).

Musztyfaga-Staszuk, M. & Honysz, R. Application of artificial neural networks in modeling of manufactured front metallization contact resistance for silicon solar cells. Arch. Metall. Mater. 60 , 1673–1678 (2015).

Sendek, A. D. et al. Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials. Energy Environ. Sci. 10 , 306–320 (2017).

Ahmad, Z., Xie, T., Maheshwari, C., Grossman, J. C. & Viswanathan, V. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes. ACS Cent. Sci. 4 , 996–1006 (2018).

Zhang, Y. et al. Unsupervised discovery of solid-state lithium ion conductors. Nat. Commun. 10 , 5260 (2019).

Doan, H. A. et al. Quantum chemistry-informed active learning to accelerate the design and discovery of sustainable energy storage materials. Chem. Mater. 32 , 6338–6346 (2020).

Chemali, E., Kollmeyer, P. J., Preindl, M. & Emadi, A. State-of-charge estimation of Li-ion batteries using deep neural networks: a machine learning approach. J. Power Sources 400 , 242–255 (2018).

Richardson, R. R., Osborne, M. A. & Howey, D. A. Gaussian process regression for forecasting battery state of health. J. Power Sources 357 , 209–219 (2017).

Berecibar, M. et al. Online state of health estimation on NMC cells based on predictive analytics. J. Power Sources 320 , 239–250 (2016).

Severson, K. A. et al. Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4 , 383–391 (2019).

Attia, P. M. et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature 578 , 397–402 (2020).

Joshi, R. P. et al. Machine learning the voltage of electrode materials in metal-ion batteries. ACS Appl. Mater. Interf. 11 , 18494–18503 (2019).

Cubuk, E. D., Sendek, A. D. & Reed, E. J. Screening billions of candidates for solid lithium-ion conductors: a transfer learning approach for small data. J. Chem. Phys. 150 , 214701 (2019).

Sendek, A. D. et al. Machine learning-assisted discovery of solid Li-ion conducting materials. Chem. Mater. 31 , 342–352 (2019).

Kim, S., Jinich, A. & Aspuru-Guzik, A. MultiDK: a multiple descriptor multiple kernel approach for molecular discovery and its application to organic flow battery electrolytes. J. Chem. Inf. Model. 57 , 657–668 (2017).

Jinich, A., Sanchez-Lengeling, B., Ren, H., Harman, R. & Aspuru-Guzik, A. A mixed quantum chemistry/machine learning approach for the fast and accurate prediction of biochemical redox potentials and its large-scale application to 315000 redox reactions. ACS Cent. Sci. 5 , 1199–1210 (2019).

Sarkar, A. et al. High entropy oxides for reversible energy storage. Nat. Commun. 9 , 3400 (2018).

Choudhury, S. et al. Stabilizing polymer electrolytes in high-voltage lithium batteries. Nat. Commun. 10 , 3091 (2019).

Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361 , 360–365 (2018).

Ng, M.-F., Zhao, J., Yan, Q., Conduit, G. J. & Seh, Z. W. Predicting the state of charge and health of batteries using data-driven machine learning. Nat. Mach. Intell. 2 , 161–170 (2020).

Steinmann, S. N. & Seh, Z. W. Understanding electrified interfaces. Nat. Rev. Mater. 6 , 289–291 (2021).

Kandasamy, N., Badrinarayanan, R., Kanamarlapudi, V., Tseng, K. & Soong, B.-H. Performance analysis of machine-learning approaches for modeling the charging/discharging profiles of stationary battery systems with non-uniform cell aging. Batteries 3 , 18 (2017).

Wei, Q., Liu, D. & Shi, G. A novel dual iterative Q-learning method for optimal battery management in smart residential environments. IEEE Trans. Ind. Electron. 62 , 2509–2518 (2015).

Murphey, Y. L. et al. Intelligent hybrid vehicle power control — Part II: online intelligent energy management. IEEE Trans. Vehicular Technol. 62 , 69–79 (2013).

Seh, Z. W. et al. Combining theory and experiment in electrocatalysis: insights into materials design. Science 355 , eaad4998 (2017).

Staffell, I. et al. The role of hydrogen and fuel cells in the global energy system. Energy Environ. Sci. 12 , 463–491 (2019).

Montoya, J. H. H. et al. Materials for solar fuels and chemicals. Nat. Mater. 16 , 70–81 (2017).

Pérez-Ramírez, J. & López, N. Strategies to break linear scaling relationships. Nat. Catal. 2 , 971–976 (2019).

Shi, C., Hansen, H. A., Lausche, A. C. & Norskov, J. K. Trends in electrochemical CO 2 reduction activity for open and close-packed metal surfaces. Phys. Chem. Chem. Phys. 16 , 4720–4727 (2014).

Calle-Vallejo, F., Loffreda, D., Koper, M. T. M. & Sautet, P. Introducing structural sensitivity into adsorption-energy scaling relations by means of coordination numbers. Nat. Chem. 7 , 403–410 (2015).

Ulissi, Z. W. et al. Machine-learning methods enable exhaustive searches for active bimetallic facets and reveal active site motifs for CO 2 reduction. ACS Catal. 7 , 6600–6608 (2017).

Nørskov, J. K., Studt, F., Abild-Pedersen, F. & Bligaard, T. Activity and selectivity maps. In Fundamental Concepts in Heterogeneous Catalysis 97–113 (John Wiley, 2014).

Garijo del Río, E., Mortensen, J. J. & Jacobsen, K. W. Local Bayesian optimizer for atomic structures. Phys. Rev. B 100 , 104103 (2019).

Jørgensen, M. S., Larsen, U. F., Jacobsen, K. W. & Hammer, B. Exploration versus exploitation in global atomistic structure optimization. J. Phys. Chem. A 122 , 1504–1509 (2018).

Jacobsen, T. L., Jørgensen, M. S. & Hammer, B. On-the-fly machine learning of atomic potential in density functional theory structure optimization. Phys. Rev. Lett. 120 , 026102 (2018).

Peterson, A. A. Acceleration of saddle-point searches with machine learning. J. Chem. Phys. 145 , 074106 (2016).

Ulissi, Z. W., Medford, A. J., Bligaard, T. & Nørskov, J. K. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat. Commun. 8 , 14621 (2017).

Huang, Y., Chen, Y., Cheng, T., Wang, L.-W. & Goddard, W. A. Identification of the selective sites for electrochemical reduction of CO to C 2+ products on copper nanoparticles by combining reactive force fields, density functional theory, and machine learning. ACS Energy Lett. 3 , 2983–2988 (2018).

Chen, Y., Huang, Y., Cheng, T. & Goddard, W. A. Identifying active sites for CO 2 reduction on dealloyed gold surfaces by combining machine learning with multiscale simulations. J. Am. Chem. Soc. 141 , 11651–11657 (2019).

Lai, Y., Jones, R. J. R., Wang, Y., Zhou, L. & Gregoire, J. M. Scanning electrochemical flow cell with online mass spectroscopy for accelerated screening of carbon dioxide reduction electrocatalysts. ACS Comb. Sci. 21 , 692–704 (2019).

Lai, Y. et al. The sensitivity of Cu for electrochemical carbon dioxide reduction to hydrocarbons as revealed by high throughput experiments. J. Mater. Chem. A 7 , 26785–26790 (2019).

Tran, K. & Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO 2 reduction and H 2 evolution. Nat. Catal. 1 , 696–703 (2018).

Zhong, M. et al. Accelerated discovery of CO 2 electrocatalysts using active machine learning. Nature 581 , 178–183 (2020).

Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571 , 95–98 (2019).

Yao, Y. et al. Carbothermal shock synthesis of high-entropy-alloy nanoparticles. Science 359 , 1489–1494 (2018).

Yao, Y. et al. High-throughput, combinatorial synthesis of multimetallic nanoclusters. Proc. Natl Acad. Sci. USA 117 , 6316–6322 (2020).

Timoshenko, J. & Roldan Cuenya, B. In situ/operando electrocatalyst characterization by X-ray absorption spectroscopy. Chem. Rev. 121 , 882–961 (2021).

Zheng, C., Chen, C., Chen, Y. & Ong, S. P. Random forest models for accurate identification of coordination environments from X-ray absorption near-edge structure. Patterns 1 , 100013–100023 (2020).

Torrisi, S. B. et al. Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships. npj Comput. Mater. 6 , 109 (2020).

Timoshenko, J. et al. Neural network approach for characterizing structural transformations by X-ray absorption fine structure spectroscopy. Phys. Rev. Lett. 120 , 225502 (2018).

Marcella, N. et al. Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy. Phys. Chem. Chem. Phys. 22 , 18902–18910 (2020).

Chen, K., Laghrouche, S. & Djerdir, A. Degradation model of proton exchange membrane fuel cell based on a novel hybrid method. Appl. Energy 252 , 113439–113447 (2019).

Ma, R. et al. Data-driven proton exchange membrane fuel cell degradation predication through deep learning method. Appl. Energy 231 , 102–115 (2018).

Jeppesen, C. et al. Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation. J. Power Sources 359 , 37–47 (2017).

Liu, J. et al. Sequence fault diagnosis for PEMFC water management subsystem using deep learning with t-SNE. IEEE Access. 7 , 92009–92019 (2019).

Ansari, M. A., Rizvi, S. M. A. & Khan, S. Optimization of electrochemical performance of a solid oxide fuel cell using artificial neural network. in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) 4230–4234 (IEEE, 2016).

Zhang, C. et al. Modelling of solid oxide electrolyser cell using extreme learning machine. Electrochim. Acta 251 , 137–144 (2017).

Esche, E., Weigert, J., Budiarto, T., Hoffmann, C. & Repke, J.-U. Optimization under uncertainty based on a data-driven model for a chloralkali electrolyzer cell. Computer-aided Chem. Eng. 46 , 577–582 (2019).

Siddaiah, R. & Saini, R. P. A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications. Renew. Sustain. Energy Rev. 58 , 376–396 (2016).

Yin, L., Yu, T., Zhang, X. & Yang, B. Relaxed deep learning for real-time economic generation dispatch and control with unified time scale. Energy 149 , 11–23 (2018).

Yu, T., Wang, H. Z., Zhou, B., Chan, K. W. & Tang, J. Multi-agent correlated equilibrium Q ( λ ) learning for coordinated smart generation control of interconnected power grids. IEEE Trans. Power Syst. 30 , 1669–1679 (2015).

Marino, D. L., Amarasinghe, K. & Manic, M. Building energy load forecasting using deep neural networks. in IECON Proceedings (Industrial Electronics Conference) 7046–7051 (IECON, 2016).

Ryu, S., Noh, J. & Kim, H. Deep neural network based demand side short term load forecasting. in 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm 2016) 308–313 (IEEE, 2016).

Mocanu, E., Nguyen, P. H., Kling, W. L. & Gibescu, M. Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning. Energy Build. 116 , 646–655 (2016).

Lund, P. D., Lindgren, J., Mikkola, J. & Salpakari, J. Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew. Sustain. Energy Rev. 45 , 785–807 (2015).

Kim, B. G., Zhang, Y., Van Der Schaar, M. & Lee, J. W. Dynamic pricing and energy consumption scheduling with reinforcement learning. IEEE Trans. Smart Grid 7 , 2187–2198 (2016).

Dusparic, I., Taylor, A., Marinescu, A., Cahill, V. & Clarke, S. Maximizing renewable energy use with decentralized residential demand response. in 2015 IEEE 1st International Smart Cities Conference (ISC2 2015 ) 1–6 (IEEE, 2015).

Dusparic, I., Harris, C., Marinescu, A., Cahill, V. & Clarke, S. Multi-agent residential demand response based on load forecasting. in 2013 1st IEEE Conference on Technologies for Sustainability (SusTech 2013) 90–96 (IEEE, 2013).

Bengio, Y., Courville, A. & Vincent, P. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 , 1798–1828 (2013).

Duvenaud, D. et al. Convolutional networks on graphs for learning molecular fingerprints. in Advances In Neural Information Processing Systems 2224–2232 (NIPS, 2015).

Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4 , 268–276 (2018).

Krenn, M., Hase, F., Nigam, A., Friederich, P. & Aspuru-Guzik, A. Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation. Mach. Learn. Sci. Technol. 1 , 045024–045031 (2020).

Jin, W., Barzilay, R. & Jaakkola, T. Junction tree variational autoencoder for molecular graph generation. Mach. Learn. 5 , 3632–3648 (2018).

You, J., Liu, B., Ying, R., Pande, V. & Leskovec, J. Graph convolutional policy network for goal-directed molecular graph generation. Adv. Neural Inf. Process. Syst. 31 , 6412–6422 (2018).

Liu, Q., Allamanis, M., Brockschmidt, M. & Gaunt, A. L. Constrained graph variational autoencoders for molecule design. in Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS’18) 7806–7815 (Curran Associates Inc., 2018).

Bartók, A. P., Kondor, R. & Csányi, G. On representing chemical environments. Phys. Rev. B 87 , 184115 (2013).

Ward, L. et al. Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations. Phys. Rev. B 96 , 024104 (2017).

Isayev, O. et al. Universal fragment descriptors for predicting properties of inorganic crystals. Nat. Commun. 8 , 15679 (2017).

Ziletti, A., Kumar, D., Scheffler, M. & Ghiringhelli, L. M. Insightful classification of crystal structures using deep learning. Nat. Commun. 9 , 2775 (2018).

Ryan, K., Lengyel, J. & Shatruk, M. Crystal structure prediction via deep learning. J. Am. Chem. Soc. 140 , 10158–10168 (2018).

Park, C. W. & Wolverton, C. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery. Phys. Rev. Mater. 4 , 063801 (2020).

Liu, X. et al. Self-Supervised Learning: Generative or Contrastive (IEEE, 2020).

Ruder, S. An overview of multi-task learning in deep neural networks. Preprint at https://doi.org/10.48550/arXiv.1706.05098 (2017).

Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: a survey. IEEE Transactions on Pattern Analysis & Machine Intelligence 44 , 5149–5169 (2020).

Vaswani, A. et al. Attention is all you need. in Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17) 6000–6010 (Curran Associates Inc., 2017).

Veličković, P. et al. Graph attention networks. Preprint at https://doi.org/10.48550/arXiv.1710.10903 (2017).

Battaglia, P. W. et al. Relational inductive biases, deep learning, and graph networks. Preprint at https://doi.org/10.48550/arXiv.1806.01261 (2018).

Satorras, V. G., Hoogeboom, E. & Welling, M. E(n) equivariant graph neural networks. Preprint at https://doi.org/10.48550/arXiv.2102.09844 (2021).

Jain, A. et al. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. Apl. Mater. 1 , 011002–011012 (2013).

Laasner, R. et al. MatD3: a database and online presentation package for research data supporting materials discovery, design, and dissemination. J. Open Source Softw. 5 , 1945–1947 (2020).

Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365 , 557 (2019).

Hachmann, J. et al. The harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid. J. Phys. Chem. Lett. 2 , 2241–2251 (2011).

Bıyık, E., Wang, K., Anari, N. & Sadigh, D. Batch active learning using determinantal point processes. Preprint at https://doi.org/10.48550/arXiv.1906.07975 (2019).

Hoffmann, J. et al. Machine learning in a data-limited regime: augmenting experiments with synthetic data uncovers order in crumpled sheets. Sci. Adv. 5 , eaau6792 (2019).

Liu, J. Z. et al. Simple and principled uncertainty estimation with deterministic deep learning via distance awareness. Adv. Neural Inf. Process Syst. 33 , 7498–7512 (2020).

Lakshminarayanan, B., Pritzel, A. & Blundell, C. Simple and scalable predictive uncertainty estimation using deep ensembles. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17) 6405–6416 (Curran Associates Inc., 2017).

D’Amour, A. et al. Underspecification presents challenges for credibility in modern machine learning. Preprint at https://doi.org/10.48550/arXiv.2011.03395 (2020).

Barredo Arrieta, A. et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion. 58 , 82–115 (2020).

Ribeiro, M. T., Singh, S. & Guestrin, C. “Why should I trust you?” Explaining the predictions of any classifier. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (2016).

Lundberg, S. & Lee, S.-I. An unexpected unity among methods for interpreting model predictions. Preprint at https://arxiv.org/abs/1611.07478 (2016).

Bach, S. et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10 , e0130140 (2015).

Sun, W. et al. The thermodynamic scale of inorganic crystalline metastability. Sci. Adv. 2 , e1600225 (2016).

Aykol, M., Dwaraknath, S. S., Sun, W. & Persson, K. A. Thermodynamic limit for synthesis of metastable inorganic materials. Sci. Adv. 4 , eaaq0148 (2018).

Wei, J. N., Duvenaud, D. & Aspuru-Guzik, A. Neural networks for the prediction of organic chemistry reactions. ACS Cent. Sci. 2 , 725–732 (2016).

Coley, C. W. et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10 , 370–377 (2019).

Nagabhushana, G. P., Shivaramaiah, R. & Navrotsky, A. Direct calorimetric verification of thermodynamic instability of lead halide hybrid perovskites. Proc. Natl Acad. Sci. USA 113 , 7717–7721 (2016).

Sanna, S. et al. Enhancement of the chemical stability in confined δ-Bi 2 O 3 . Nat. Mater. 14 , 500–504 (2015).

Podolyan, Y., Walters, M. A. & Karypis, G. Assessing synthetic accessibility of chemical compounds using machine learning methods. J. Chem. Inf. Model. 50 , 979–991 (2010).

Coley, C. W., Rogers, L., Green, W. H. & Jensen, K. F. SCScore: synthetic complexity learned from a reaction corpus. J. Chem. Inf. Model. 58 , 252–261 (2018).

Kim, E. et al. Inorganic materials synthesis planning with literature-trained neural networks. J. Chem. Inf. Model. 60 , 1194–1201 (2020).

Huo, H. et al. Semi-supervised machine-learning classification of materials synthesis procedures. npj Comput. Mater. 5 , 62 (2019).

Tian, C., Lin, F. & Doeff, M. M. Electrochemical characteristics of layered transition metal oxide cathode materials for lithium ion batteries: surface, bulk behavior, and thermal properties. Acc. Chem. Res. 51 , 89–96 (2018).

Guilminot, E., Corcella, A., Charlot, F., Maillard, F. & Chatenet, M. Detection of Pt z + ions and Pt nanoparticles inside the membrane of a used PEMFC. J. Electrochem. Soc. 154 , B96 (2007).

Pender, J. P. et al. Electrode degradation in lithium-ion batteries. ACS Nano 14 , 1243–1295 (2020).

Li, Y. et al. Atomic structure of sensitive battery materials and interfaces revealed by cryo-electron microscopy. Science 358 , 506–510 (2017).

Wang, H. Numerical modeling of non-planar hydraulic fracture propagation in brittle and ductile rocks using XFEM with cohesive zone method. J. Pet. Sci. Eng. 135 , 127–140 (2015).

Hsu, Y.-C., Yu, C.-H. & Buehler, M. J. Using deep learning to predict fracture patterns in crystalline solids. Matter 3 , 197–211 (2020).

Wuest, T., Weimer, D., Irgens, C. & Thoben, K. D. Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4 , 23–45 (2016).

De Jong, P., Sánchez, A. S., Esquerre, K., Kalid, R. A. & Torres, E. A. Solar and wind energy production in relation to the electricity load curve and hydroelectricity in the northeast region of Brazil. Renew. Sustain. Energy Rev. 23 , 526–535 (2013).

Zolfani, S. H. & Saparauskas, J. New application of SWARA method in prioritizing sustainability assessment indicators of energy system. Eng. Econ. 24 , 408–414 (2013).

Tao, F., Zhang, M., Liu, Y. & Nee, A. Y. C. Digital twin driven prognostics and health management for complex equipment. CIRP Ann. 67 , 169–172 (2018).

Yun, S., Park, J. H. & Kim, W. T. Data-centric middleware based digital twin platform for dependable cyber-physical systems. in International Conference on Ubiquitous and Future Networks (ICUFN) 922–926 (2017).

Boretti, A. Integration of solar thermal and photovoltaic, wind, and battery energy storage through AI in NEOM city. Energy AI 3 , 100038–100045 (2021).

Ghoddusi, H., Creamer, G. G. & Rafizadeh, N. Machine learning in energy economics and finance: a review. Energy Econ. 81 , 709–727 (2019).

Asensio, O. I., Mi, X. & Dharur, S. Using machine learning techniques to aid environmental policy analysis: a teaching case regarding big data and electric vehicle charging infrastructure. Case Stud. Environ. 4 , 961302 (2020).

Zheng, S., Trott, A., Srinivasa, S., Parkes, D. C. & Socher, R. The AI economist: taxation policy design via two-level deep multiagent reinforcement learning. Sci. Adv. 8 , eabk2607 (2022).

Sun, S. et al. A data fusion approach to optimize compositional stability of halide perovskites. Matter 4 , 1305–1322 (2021).

Sun, W. et al. Machine learning — assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials. Sci. Adv. 5 , eaay4275 (2019).

Sun, S. et al. Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis. Joule 3 , 1437–1451 (2019).

Kirman, J. et al. Machine-learning-accelerated perovskite crystallization. Matter 2 , 938–947 (2020).

Langner, S. et al. Beyond ternary OPV: high-throughput experimentation and self-driving laboratories optimize multicomponent systems. Adv. Mater. 32 , 1907801 (2020).

Hartono, N. T. P. et al. How machine learning can help select capping layers to suppress perovskite degradation. Nat. Commun. 11 , 4172 (2020).

Odabaşı, Ç. & Yıldırım, R. Performance analysis of perovskite solar cells in 2013–2018 using machine-learning tools. Nano Energy 56 , 770–791 (2019).

Fenning, D. P. et al. Darwin at high temperature: advancing solar cell material design using defect kinetics simulations and evolutionary optimization. Adv. Energy Mater. 4 , 1400459 (2014).

Allam, O., Cho, B. W., Kim, K. C. & Jang, S. S. Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries. RSC Adv. 8 , 39414–39420 (2018).

Okamoto, Y. & Kubo, Y. Ab initio calculations of the redox potentials of additives for lithium-ion batteries and their prediction through machine learning. ACS Omega 3 , 7868–7874 (2018).

Takagishi, Y., Yamanaka, T. & Yamaue, T. Machine learning approaches for designing mesoscale structure of Li-ion battery electrodes. Batteries 5 , 54 (2019).

Tan, Y., Liu, W. & Qiu, Q. Adaptive power management using reinforcement learning. in IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers (ICCAD ) 461–467 (IEEE, 2009).

Ermon, S., Xue, Y., Gomes, C. & Selman, B. Learning policies for battery usage optimization in electric vehicles. Mach. Learn. 92 , 177–194 (2013).

Schmidt, J., Marques, M. R. G., Botti, S. & Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 5 , 83 (2019).

Pilania, G. Machine learning in materials science: from explainable predictions to autonomous design. Comput. Mater. Sci. 193 , 110360 (2021).

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559 , 547–555 (2018).

Kaufmann, K. et al. Crystal symmetry determination in electron diffraction using machine learning. Science 367 , 564–568 (2020).

Chen, C., Zuo, Y., Ye, W., Li, X. & Ong, S. P. Learning properties of ordered and disordered materials from multi-fidelity data. Nat. Comput. Sci. 1 , 46–53 (2021).

Liu, M., Yan, K., Oztekin, B. & Ji, S. GraphEBM: molecular graph generation with energy-based models. Preprint at https://doi.org/10.48550/arXiv.2102.00546 (2021).

Segler, M. H. S., Preuss, M. & Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555 , 604–610 (2018).

Granda, J. M., Donina, L., Dragone, V., Long, D.-L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559 , 377–381 (2018).

Epps, R. W. et al. Artificial chemist: an autonomous quantum dot synthesis bot. Adv. Mater. 32 , 2001626 (2020).

MacLeod, B. P. et al. Self-driving laboratory for accelerated discovery of thin-film materials. Sci. Adv. 6 , eaaz8867 (2020).

Li, Z. et al. Robot-accelerated perovskite investigation and discovery. Chem. Mater. 32 , 5650–5663 (2020).

Aguiar, J. A., Gong, M. L., Unocic, R. R., Tasdizen, T. & Miller, B. D. Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning. Sci. Adv. 5 , eaaw1949 (2019).

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Acknowledgements

Z.Y. and A.A.-G. were supported as part of the Nanoporous Materials Genome Center by the US Department of Energy, Office of Science, Office of Basic Energy Sciences under award number DE-FG02-17ER16362 and the US Department of Energy, Office of Science — Chicago under award number DE-SC0019300. A.J. was financially supported by Huawei Technologies Canada and the Natural Sciences and Engineering Research Council (NSERC). L.M.M.-M. thanks the support of the Defense Advanced Research Projects Agency under the Accelerated Molecular Discovery Program under cooperative agreement number HR00111920027 dated 1 August 2019. Y.W. acknowledges funding support from the Singapore National Research Foundation under its Green Buildings Innovation Cluster (GBIC award number NRF2015ENC-GBICRD001-012) administered by the Building and Construction Authority, its Green Data Centre Research (GDCR award number NRF2015ENC-GDCR01001-003) administered by the Info-communications Media Development Authority, and its Energy Programme (EP award number NRF2017EWT-EP003-023) administered by the Energy Market Authority of Singapore. A.A.-G. is a Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow. E.H.S. acknowledges funding by the Ontario Ministry of Colleges and Universities (grant ORF-RE08-034), the Natural Sciences and Engineering Research Council (NSERC) of Canada (grant RGPIN-2017-06477), the Canadian Institute for Advanced Research (CIFAR) (grant FS20-154 APPT.2378) and the University of Toronto Connaught Fund (grant GC 2012-13). Z.W.S. acknowledges funding by the Singapore National Research Foundation (NRF-NRFF2017-04).

Author information

These authors contributed equally: Zhenpeng Yao, Yanwei Lum, Andrew Johnston.

Authors and Affiliations

Shanghai Key Laboratory of Hydrogen Science & Center of Hydrogen Science, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Zhenpeng Yao

Chemical Physics Theory Group, Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

Zhenpeng Yao, Luis Martin Mejia-Mendoza & Alán Aspuru-Guzik

Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China

State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Innovis, Singapore, Singapore

Yanwei Lum & Zhi Wei Seh

Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada

Yanwei Lum, Andrew Johnston & Edward H. Sargent

School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

Xin Zhou & Yonggang Wen

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Yao, Z., Lum, Y., Johnston, A. et al. Machine learning for a sustainable energy future. Nat Rev Mater 8 , 202–215 (2023). https://doi.org/10.1038/s41578-022-00490-5

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Exploring the role of fossil fuels and renewable energy in determining environmental sustainability: evidence from oecd countries.

1. Introduction

2. literature review, 3. data and methodology, 3.2. estimation technique, 3.2.1. cross-sectional dependence, 3.2.2. unit root tests for panel data, 3.2.3. panel cointegration test, 3.2.4. cs-ardl estimation, 4. results and discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Paramati, S.R.; Shahzad, U.; Doğan, B. The role of environmental technology for energy demand and energy efficiency: Evidence from OECD countries. Renew. Sustain. Energy Rev. 2022 , 153 , 111735. [ Google Scholar ] [ CrossRef ]
• Kaya, Y. Impact of Carbon Dioxide Emission Control on Gnp Growth: Interpretation of Proposed Scenarios ; Intergovernmental Panel on Climate Change/Response Strategies Working Group: Geneva, Switzerland, 1989. [ Google Scholar ]
• Payne, A. Handbook of CRM ; Routledge: London, UK, 2012. [ Google Scholar ]
• Salim, R.A.; Rafiq, S. Why do some emerging economies proactively accelerate the adoption of renewable energy? Energy Econ. 2012 , 34 , 1051–1057. [ Google Scholar ] [ CrossRef ]
• Apergis, N.; Payne, J.E. Renewable energy, output, CO 2 emissions, and fossil fuel prices in Central America: Evidence from a nonlinear panel smooth transition vector error correction model. Energy Econ. 2014 , 42 , 226–232. [ Google Scholar ] [ CrossRef ]
• Bildirici, M.; Ersin, Ö.Ö. Economic growth and CO 2 emissions: An investigation with smooth transition autoregressive distributed lag models for the 1800–2014 period in the USA. Environ. Sci. Pollut. Res. 2018 , 25 , 200–219. [ Google Scholar ] [ CrossRef ]
• Yu, S.; Hu, X.; Li, L.; Chen, H. Does the development of renewable energy promote carbon reduction? Evidence from Chinese provinces. J. Environ. Manag. 2020 , 268 , 110634. [ Google Scholar ] [ CrossRef ]
• Huang, S.-Z.; Chien, F.; Sadiq, M. A gateway towards a sustainable environment in emerging countries: The nexus between green energy and human Capital. Econ. Res.-Ekon. Istraž. 2022 , 35 , 4159–4176. [ Google Scholar ] [ CrossRef ]
• Abbasi, S.; Noorzai, E. The BIM-Based multi-optimization approach in order to determine the trade-off between embodied and operation energy focused on renewable energy use. J. Clean. Prod. 2021 , 281 , 125359. [ Google Scholar ] [ CrossRef ]
• Sadorsky, P. Renewable energy consumption and income in emerging economies. Energy Policy 2009 , 37 , 4021–4028. [ Google Scholar ] [ CrossRef ]
• Sadorsky, P. Renewable energy consumption, CO 2 emissions and oil prices in the G7 countries. Energy Econ. 2009 , 31 , 456–462. [ Google Scholar ] [ CrossRef ]
• Sebri, M.; Ben-Salha, O. On the causal dynamics between economic growth, renewable energy consumption, CO 2 emissions and trade openness: Fresh evidence from BRICS countries. Renew. Sustain. Energy Rev. 2014 , 39 , 14–23. [ Google Scholar ] [ CrossRef ]
• Lu, W.-C. The impacts of information and communication technology, energy consumption, financial development, and economic growth on carbon dioxide emissions in 12 Asian countries. Mitig. Adapt. Strateg. Glob. Chang. 2018 , 23 , 1351–1365. [ Google Scholar ] [ CrossRef ]
• Ito, K. CO 2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries. Int. Econ. 2017 , 151 , 1–6. [ Google Scholar ] [ CrossRef ]
• Ersin, Ö.; Bildirici, M. Asymmetry in the environmental pollution, economic development and petrol price relationship: MRS-VAR and nonlinear causality analyses. Rom. J. Econ. 2019 , 22 , 25–50. [ Google Scholar ]
• Ike, G.N.; Usman, O.; Alola, A.A.; Sarkodie, S.A. Environmental quality effects of income, energy prices and trade: The role of renewable energy consumption in G-7 countries. Sci. Total Environ. 2020 , 721 , 137813. [ Google Scholar ] [ CrossRef ]
• Rehman, A.; Ma, H.; Ahmad, M.; Ozturk, I.; Işık, C. An asymmetrical analysis to explore the dynamic impacts of CO 2 emission to renewable energy, expenditures, foreign direct investment, and trade in Pakistan. Environ. Sci. Pollut. Res. 2021 , 28 , 53520–53532. [ Google Scholar ] [ CrossRef ]
• Khan, K.; Su, C.W.; Rehman, A.U.; Ullah, R. Is technological innovation a driver of renewable energy? Technol. Soc. 2022 , 70 , 102044. [ Google Scholar ] [ CrossRef ]
• Raihan, A.; Voumik, L.C. Carbon emission dynamics in India due to financial development, renewable energy utilization, technological innovation, economic growth, and urbanization. J. Environ. Sci. Econ. 2022 , 1 , 36–50. [ Google Scholar ] [ CrossRef ]
• Abbasi, K.R.; Hussain, K.; Haddad, A.M.; Salman, A.; Ozturk, I. The role of financial development and technological innovation towards sustainable development in Pakistan: Fresh insights from consumption and territory-based emissions. Technol. Forecast. Soc. Chang. 2022 , 176 , 121444. [ Google Scholar ] [ CrossRef ]
• Westerlund, J.; Edgerton, D.L. A simple test for cointegration in dependent panels with structural breaks. Oxf. Bull. Econ. Stat. 2008 , 70 , 665–704. [ Google Scholar ] [ CrossRef ]
• Dumitrescu, E.-I.; Hurlin, C. Testing for Granger non-causality in heterogeneous panels. Econ. Model. 2012 , 29 , 1450–1460. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Lin, Y.-L.; Zheng, N.-Y.; Lin, C.-S. Repurposing Washingtonia filifera petiole and Sterculia foetida follicle waste biomass for renewable energy through torrefaction. Energy 2021 , 223 , 120101. [ Google Scholar ] [ CrossRef ]
• Zafar, M.W.; Saleem, M.M.; Destek, M.A.; Caglar, A.E. The dynamic linkage between remittances, export diversification, education, renewable energy consumption, economic growth, and CO 2 emissions in top remittance-receiving countries. Sustain. Dev. 2022 , 30 , 165–175. [ Google Scholar ] [ CrossRef ]
• Tiwari, A.K. A structural VAR analysis of renewable energy consumption, real GDP and CO 2 emissions: Evidence from India. Econ. Bull. 2011 , 31 , 1793–1806. [ Google Scholar ]
• Zhang, Q.; Oo, B.L.; Lim, B.T.H. Linking corporate social responsibility (CSR) practices and organizational performance in the construction industry: A resource collaboration network. Resour. Conserv. Recycl. 2022 , 179 , 106113. [ Google Scholar ] [ CrossRef ]
• Hanif, I. Impact of economic growth, nonrenewable and renewable energy consumption, and urbanization on carbon emissions in Sub-Saharan Africa. Environ. Sci. Pollut. Res. 2018 , 25 , 15057–15067. [ Google Scholar ] [ CrossRef ]
• Chen, Y.; Wang, Z.; Zhong, Z. CO 2 emissions, economic growth, renewable and non-renewable energy production and foreign trade in China. Renew. Energy 2019 , 131 , 208–216. [ Google Scholar ] [ CrossRef ]
• Wang, J.; Zhang, S.; Zhang, Q. The relationship of renewable energy consumption to financial development and economic growth in China. Renew. Energy 2021 , 170 , 897–904. [ Google Scholar ] [ CrossRef ]
• Jun, W.; Mahmood, H.; Zakaria, M. Impact of trade openness on environment in China. J. Bus. Econ. Manag. 2020 , 21 , 1185–1202. [ Google Scholar ] [ CrossRef ]
• Cheng, Y.; Yao, X. Carbon intensity reduction assessment of renewable energy technology innovation in China: A panel data model with cross-section dependence and slope heterogeneity. Renew. Sustain. Energy Rev. 2021 , 135 , 110157. [ Google Scholar ] [ CrossRef ]
• Mongo, M.; Belaid, F.; Ramdani, B. The effects of environmental innovations on CO 2 emissions: Empirical evidence from Europe. Environ. Sci. Policy 2021 , 118 , 1–9. [ Google Scholar ] [ CrossRef ]
• Adebayo, T.S.; Rjoub, H.; Akinsola, G.D.; Oladipupo, S.D. The asymmetric effects of renewable energy consumption and trade openness on carbon emissions in Sweden: New evidence from quantile-on-quantile regression approach. Environ. Sci. Pollut. Res. 2022 , 29 , 1875–1886. [ Google Scholar ] [ CrossRef ]
• Raihan, A.; Tuspekova, A. Toward a sustainable environment: Nexus between economic growth, renewable energy use, forested area, and carbon emissions in Malaysia. Resour. Conserv. Recycl. Adv. 2022 , 15 , 200096. [ Google Scholar ] [ CrossRef ]
• Zhao, B.; Yang, W. Does financial development influence CO 2 emissions? A Chinese province-level study. Energy 2020 , 200 , 117523. [ Google Scholar ] [ CrossRef ]
• Baloch, M.A.; Ozturk, I.; Bekun, F.V.; Khan, D. Modeling the dynamic linkage between financial development, energy innovation, and environmental quality: Does globalization matter? Bus. Strategy Environ. 2021 , 30 , 176–184. [ Google Scholar ] [ CrossRef ]
• Wen, J.; Mahmood, H.; Khalid, S.; Zakaria, M. The impact of financial development on economic indicators: A dynamic panel data analysis. Econ. Res.-Ekon. Istraž. 2021 , 35 , 2930–2942. [ Google Scholar ] [ CrossRef ]
• Bakhsh, S.; Yin, H.; Shabir, M. Foreign investment and CO 2 emissions: Do technological innovation and institutional quality matter? Evidence from system GMM approach. Environ. Sci. Pollut. Res. 2021 , 28 , 19424–19438. [ Google Scholar ] [ CrossRef ]
• Jafri, M.A.H.; Abbas, S.; Abbas, S.M.Y.; Ullah, S. Caring for the environment: Measuring the dynamic impact of remittances and FDI on CO 2 emissions in China. Environ. Sci. Pollut. Res. 2022 , 29 , 9164–9172. [ Google Scholar ] [ CrossRef ]
• Jun, W.; Zakaria, M.; Shahzad, S.J.H.; Mahmood, H. Effect of FDI on pollution in China: New insights based on wavelet approach. Sustainability 2018 , 10 , 3859. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mishra, H.G.; Pandita, S.; Bhat, A.A.; Mishra, R.K.; Sharma, S. Tourism and carbon emissions: A bibliometric review of the last three decades: 1990–2021. Tour. Rev. 2021 , 77 , 636–658. [ Google Scholar ] [ CrossRef ]
• Nosheen, M.; Iqbal, J.; Khan, H.U. Analyzing the linkage among CO 2 emissions, economic growth, tourism, and energy consumption in the Asian economies. Environ. Sci. Pollut. Res. 2021 , 28 , 16707–16719. [ Google Scholar ] [ CrossRef ]
• Wei, L.; Ullah, S. International tourism, digital infrastructure, and CO 2 emissions: Fresh evidence from panel quantile regression approach. Environ. Sci. Pollut. Res. 2022 , 29 , 36273–36280. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yildirim, E.; Aslan, A. Energy consumption and economic growth nexus for 17 highly developed OECD countries: Further evidence based on bootstrap-corrected causality tests. Energy Policy 2012 , 51 , 985–993. [ Google Scholar ] [ CrossRef ]
• Apergis, N.; Payne, J.E. The causal dynamics between renewable energy, real GDP, emissions and oil prices: Evidence from OECD countries. Appl. Econ. 2014 , 46 , 4519–4525. [ Google Scholar ] [ CrossRef ]
• Lu, X.; Zhang, L.; Chen, Y.; Zhou, M.; Zheng, B.; Li, K.; Liu, Y.; Lin, J.; Fu, T.-M.; Zhang, Q. Exploring 2016–2017 surface ozone pollution over China: Source contributions and meteorological influences. Atmos. Chem. Phys. 2019 , 19 , 8339–8361. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Arouri, M.E.H.; Youssef, A.B.; M’henni, H.; Rault, C. Energy consumption, economic growth and CO 2 emissions in Middle East and North African countries. Energy Policy 2012 , 45 , 342–349. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Apergis, N.; Payne, J.E. Renewable energy, output, carbon dioxide emissions, and oil prices: Evidence from South America. Energy Sources Part B Econ. Plan. Policy 2015 , 10 , 281–287. [ Google Scholar ] [ CrossRef ]
• Riti, J.S.; Song, D.; Shu, Y.; Kamah, M. Decoupling CO 2 emission and economic growth in China: Is there consistency in estimation results in analyzing environmental Kuznets curve? J. Clean. Prod. 2017 , 166 , 1448–1461. [ Google Scholar ] [ CrossRef ]
• Ahmed, Z.; Ahmad, M.; Rjoub, H.; Kalugina, O.A.; Hussain, N. Economic growth, renewable energy consumption, and ecological footprint: Exploring the role of environmental regulations and democracy in sustainable development. Sustain. Dev. 2022 , 30 , 595–605. [ Google Scholar ] [ CrossRef ]
• Dagar, V.; Khan, M.K.; Alvarado, R.; Rehman, A.; Irfan, M.; Adekoya, O.B.; Fahad, S. Impact of renewable energy consumption, financial development and natural resources on environmental degradation in OECD countries with dynamic panel data. Environ. Sci. Pollut. Res. 2022 , 29 , 18202–18212. [ Google Scholar ] [ CrossRef ]
• Benli, M. The Long-Run Effects of Trade and Income on Carbon Emissions: Evidence from Heterogeneous Dynamic Panel of Developing Countries. Balk. Near East. J. Soc. Sci. 2019 , 5 , 51–58. [ Google Scholar ]
• Safi, A.; Chen, Y.; Wahab, S.; Ali, S.; Yi, X.; Imran, M. Financial instability and consumption-based carbon emission in E-7 countries: The role of trade and economic growth. Sustain. Prod. Consum. 2021 , 27 , 383–391. [ Google Scholar ] [ CrossRef ]
• Pesaran, M.H. Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 2006 , 74 , 967–1012. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Pesaran, M.H.; Yamagata, T. Testing slope homogeneity in large panels. J. Econ. 2007 , 142 , 50–93. [ Google Scholar ] [ CrossRef ]
• Breusch, T.S.; Pagan, A.R. The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics. Rev. Econ. Stud. 1980 , 47 , 239–253. [ Google Scholar ] [ CrossRef ]
• Lv, Z.; Xu, T. Is economic globalization good or bad for the environmental quality? New evidence from dynamic heterogeneous panel models. Technol. Forecast. Soc. Chang. 2018 , 137 , 340–343. [ Google Scholar ] [ CrossRef ]
• Pesaran, M.H. A simple panel unit root test in the presence of cross-section dependence. J. Appl. Econ. 2007 , 22 , 265–312. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• McCoskey, S.; Kao, C. A residual-based test of the null of cointegration in panel data. Econ. Rev. 1998 , 17 , 57–84. [ Google Scholar ] [ CrossRef ]
• Pedroni, P. Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econ. Theory 2004 , 20 , 597–625. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Westerlund, J. New simple tests for panel cointegration. Econ. Rev. 2005 , 24 , 297–316. [ Google Scholar ] [ CrossRef ]
• Westerlund, J. Testing for error correction in panel data. Oxf. Bull. Econ. Stat. 2007 , 69 , 709–748. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Phillips, P.C.B.; Sul, D. Dynamic Panel Estimation and Homogeneity Testing under Cross Dynamic Panel Estimation and Homogeneity Testing under Cross Section Dependence Section Dependence. 2002. Available online: https://elischolar.library.yale.edu/cowles-discussion-paper-series/1626 (accessed on 10 November 2022).
• Xiaoman, W.; Majeed, A.; Vasbieva, D.G.; Yameogo, C.E.W.; Hussain, N. Natural resources abundance, economic globalization, and carbon emissions: Advancing sustainable development agenda. Sustain. Dev. 2021 , 29 , 1037–1048. [ Google Scholar ] [ CrossRef ]
• Liu, J.; Murshed, M.; Chen, F.; Shahbaz, M.; Kirikkaleli, D.; Khan, Z. An empirical analysis of the household consumption-induced carbon emissions in China. Sustain. Prod. Consum. 2021 , 26 , 943–957. [ Google Scholar ] [ CrossRef ]
• Mehmood, U. Biomass energy consumption and its impacts on ecological footprints: Analyzing the role of globalization and natural resources in the framework of EKC in SAARC countries. Environ. Sci. Pollut. Res. 2022 , 29 , 17513–17519. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Khan, Z.; Ali, S.; Umar, M.; Kirikkaleli, D.; Jiao, Z. Consumption-based carbon emissions and International trade in G7 countries: The role of Environmental innovation and Renewable energy. Sci. Total Environ. 2020 , 730 , 138945. [ Google Scholar ] [ CrossRef ]

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Hou, H.; Lu, W.; Liu, B.; Hassanein, Z.; Mahmood, H.; Khalid, S. Exploring the Role of Fossil Fuels and Renewable Energy in Determining Environmental Sustainability: Evidence from OECD Countries. Sustainability 2023 , 15 , 2048. https://doi.org/10.3390/su15032048

Hou H, Lu W, Liu B, Hassanein Z, Mahmood H, Khalid S. Exploring the Role of Fossil Fuels and Renewable Energy in Determining Environmental Sustainability: Evidence from OECD Countries. Sustainability . 2023; 15(3):2048. https://doi.org/10.3390/su15032048

Hou, Haitao, Wei Lu, Bing Liu, Zeina Hassanein, Hamid Mahmood, and Samia Khalid. 2023. "Exploring the Role of Fossil Fuels and Renewable Energy in Determining Environmental Sustainability: Evidence from OECD Countries" Sustainability 15, no. 3: 2048. https://doi.org/10.3390/su15032048

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Alcohol fuels in SI engines: a comprehensive state-of-the-art review on combustion, performance, and environmental impacts

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The search for alternative fuels compatible with internal combustion engines has escalated as a result of worldwide pollution and the exhaustion of fossil resources. Alcoholic fuels, such as methanol, ethanol, butanol, and fusel alcohols, are being considered as viable alternatives to gasoline and gaseous fuels. This review analyzes the effects of alcoholic fuels on the performance, combustion, and emissions of spark-ignition (SI) engines. It specifically focuses on several fuel supply modes, including blending, dual mode, and dedicated (100%) modes. This paper examines the impact of fuel characteristics on engine parameters and investigates various operating settings to improve performance. Furthermore, it tackles the existing difficulties linked to the use of alcoholic fuel blends in spark-ignition (SI) engines and puts forward alternative remedies. Special emphasis is placed on ethanol, which has shown to possess adequate fuel mixture characteristics for spark-ignition (SI) engines in current circumstances. The analysis identifies deficiencies in current research, particularly concerning the combustion of fusel alcohol in direct injection (DI) and port-fuel injection (PFI) engines. It underscores the necessity for more investigations into the long-term durability of engines and the compatibility of materials with alcohol fuels. This article intends to provide a thorough overview that will direct future research and development endeavors toward achieving a more sustainable and efficient utilization of alcoholic fuels in internal combustion engines.

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Alcohol Fuels in Spark Ignition Engines

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Abbreviations

Acetone/butanol/ethanol

ABE direct injection ratio

ABE port injection ratio

Alcohol fuel energy substitution ratio

Air/fuel ratio

Accumulation mode particle number

Brake mean effective pressure

Brake specific fuel consumption

Brake torque

Before top-dead center

Brake thermal efficiency

Carbon to hydrogen ratio

Crank angle

Crank angle degree

Cooperative fuel research

Cumulative heat release

Carbon monoxide

Carbon dioxide

Coefficient of variation in IMEP

Coefficient of variation of IMEP

Compression ratio

Direct fuel injection

Dual-fuel spark ignition

Direct injection

Direct injection ratio

Direct injection timing

Direct injected turbocharged

Electronic fuel injected

Exhaust gas recirculation

Exhaust gas temperature

Fusel oil after water extracted

Fusel oil before water extracted

Flame kernel radius

Gasoline direct injection

Gasoline intake port injection combined with n-butanol direct injection

Gasoline port injection and n-butanol direct injection

Gasoline port injection

Hydrogen blending ratio

Hydrocarbon

Homogeneous charge compression ignition

Heat release rate

I-propanol-n-butanol/ethanol

Iso-butanol–bio-methanol to gasoline

Initial combustion duration

Internal combustion engine

Initial combustion period

In-cylinder pressure

Indicated thermal efficiency

Laminar burning velocity

Lower calorific value

Lower heating value

Minimum spark timing for best torque

Major combustion duration

Methanol energy substitution ratio

N-butanol–bio-ethanol to gasoline

Natural gas

N-butanol port injection and gasoline direct injection

Port injection combined with gasoline direct injection

Nitrogen monoxide

Nitrogen oxide

Nucleation mode particle number

Peak cylinder pressure

Port-fuel injection

Pure gasoline

Particulate matter

Particle number

Primary reference fuels

Support vector regression

Strength weakness opportunities threats

Top-dead center

Total particulate matter

Total particle number

Toluene primary reference fuels

Unburned hydrocarbon

Wide open throttle

Sinigaglia T, Lewiski F, Santos Martins ME, Mairesse Siluk JC. Production, storage, fuel stations of hydrogen and its utilization in automotive applications-a review. Int J Hydrogen Energy. 2017;42:24597–611.

Article   CAS   Google Scholar  

Kim Y, Kawahara N, Tsuboi K, Tomita E. Combustion characteristics and NOX emissions of biogas fuels with various CO2 contents in a micro co-generation spark-ignition engine. Appl Energy. 2016;182:539–47.

Masum BM, Masjuki HH, Kalam MA, Rizwanul Fattah IM, Palash SM, Abedin MJ. Effect of ethanol–gasoline blend on NOx emission in SI engine. Renew Sustain Energy Rev. 2013;24:209–22.

Elagouz N, Onat NC, Kucukvar M, Sen B, Kutty AA, Kagawa S, et al. Rethinking mobility strategies for mega-sporting events: a global multiregional input-output-based hybrid life cycle sustainability assessment of alternative fuel bus technologies. Sustain Prod Consum. 2022;33:767–87.

Article   Google Scholar  

Thangavelu SK, Ahmed AS, Ani FN. Review on bioethanol as alternative fuel for spark ignition engines. Renew Sustain Energy Rev. 2016;56:820–35.

Kumar S, Cho JH, Park J, Moon I. Advances in diesel–alcohol blends and their effects on the performance and emissions of diesel engines. Renew Sustain Energy Rev. 2013;22:46–72.

Li Y, Gong J, Deng Y, Yuan W, Fu J, Zhang B. Experimental comparative study on combustion, performance and emissions characteristics of methanol, ethanol and butanol in a spark ignition engine. Appl Therm Eng. 2017;115:53–63.

Göktaş M, Kemal Balki M, Sayin C, Canakci M. An evaluation of the use of alcohol fuels in SI engines in terms of performance, emission and combustion characteristics: a review. Fuel. 2021;286: 119425.

Deng B, Yang J, Zhang D, Feng R, Fu J, Liu J, et al. The challenges and strategies of butanol application in conventional engines: the sensitivity study of ignition and valve timing. Appl Energy. 2013;108:248–60.

Thakur AK, Kaviti AK, Mehra R, Mer KKS. Progress in performance analysis of ethanol-gasoline blends on SI engine. Renew Sustain Energy Rev. 2017;69:324–40.

Bai X, Xu M, Li Q, Yu L. Trajectory-battery integrated design and its application to orbital maneuvers with electric pump-fed engines. Adv Space Res. 2022;70:825–41.

Ghadikolaei MA. Effect of alcohol blend and fumigation on regulated and unregulated emissions of IC engines—a review. Renew Sustain Energy Rev. 2016;57:1440–95.

Awad OI, Mamat R, Ali OM, Sidik NAC, Yusaf T, Kadirgama K, et al. Alcohol and ether as alternative fuels in spark ignition engine: a review. Renew Sustain Energy Rev. 2018;82:2586–605.

Yaqoob H, Teoh YH, Sher F, Jamil MA, Ali M, Ağbulut Ü, et al. Energy, exergy, sustainability and economic analysis of waste tire pyrolysis oil blends with different nanoparticle additives in spark ignition engine. Energy. 2022;251: 123697.

Verhelst S, Turner JW, Sileghem L, Vancoillie J. Methanol as a fuel for internal combustion engines. Prog Energy Combust Sci. 2019;70:43–88.

Sharudin H, Abdullah NR, Najafi G, Mamat R, Masjuki HH. Investigation of the effects of iso-butanol additives on spark ignition engine fuelled with methanol-gasoline blends. Appl Therm Eng. 2017;114:593–600.

Cesur I. Investigation of the effects of water injection into an SI engine running on M15 methanol fuel on engine performance and exhaust emissions. Energy. 2022;261: 125203.

Amine M, Barakat Y. Properties of gasoline-ethanol-methanol ternary fuel blend compared with ethanol-gasoline and methanol-gasoline fuel blends. Egypt J Pet. 2019;28:371–6.

Gong C, Li Z, Chen Y, Liu J, Liu F, Han Y. Influence of ignition timing on combustion and emissions of a spark-ignition methanol engine with added hydrogen under lean-burn conditions. Fuel. 2019;235:227–38.

Bielaczyc P, Woodburn J, Klimkiewicz D, Pajdowski P, Szczotka A. An examination of the effect of ethanol–gasoline blends’ physicochemical properties on emissions from a light-duty spark ignition engine. Fuel Process Technol. 2013;107:50–63.

Yüksel F, Yüksel B. The use of ethanol–gasoline blend as a fuel in an SI engine. Renew Energy. 2004;29:1181–91.

Zaharin MSM, Abdullah NR, Masjuki HH, Ali OM, Najafi G, Yusaf T. Evaluation on physicochemical properties of iso-butanol additives in ethanol-gasoline blend on performance and emission characteristics of a spark-ignition engine. Appl Therm Eng. 2018;144:960–71.

Leone TG, Anderson JE, Davis RS, Iqbal A, Reese RA, Shelby MH, et al. The effect of compression ratio, fuel octane rating, and ethanol content on spark-ignition engine efficiency. Environ Sci Technol. 2015;49:10778–89.

Article   CAS   PubMed   Google Scholar  

Calvin YL, Hariyanto PAT, Usman AI, Masuku M, Wibowo CS, Maymuchar, et al. Volatility and physicochemical properties of gasoline-ethanol blends with gasoline RON-based 88, 90, and 92. Fuel. 2022;307: 121850.

Loyte A, Suryawanshi J, Bhiogade G, Devarajan Y, Subbiah G. Recent developments in utilizing hydrous ethanol for diverse engine technologies. Chem Eng Process - Process Intensif. 2022;177: 108985.

da Silva Trindade WR, dos Santos RG. 1D modeling of SI engine using n-butanol as fuel: Adjust of fuel properties and comparison between measurements and simulation. Energy Convers Manag. 2018;157:224–38.

Veza I, . Said MF, . Latiff Z MA. Improved performance, combustion and emissions of si engine fuelled with butanol: a review. Int J Automot Mech Eng. 2020;17:7648–66.

Li Y, Tang W, Chen Y, Liu J, Lee CF. Potential of acetone-butanol-ethanol (ABE) as a biofuel. Fuel. 2019;242:673–86.

Agbro E, Zhang W, Tomlin AS, Burluka A. Experimental Study on the Influence of n -butanol blending on the combustion, autoignition, and knock properties of gasoline and its surrogate in a spark-ignition engine. Energy Fuel. 2018;32:10052–64.

Awad OI, Ali OM, Mamat R, Abdullah AA, Najafi G, Kamarulzaman MK, et al. Using fusel oil as a blend in gasoline to improve SI engine efficiencies: a comprehensive review. Renew Sustain Energy Rev. 2017;69:1232–42.

Safieddin Ardebili SM, Solmaz H, İpci D, Calam A, Mostafaei M. A review on higher alcohol of fusel oil as a renewable fuel for internal combustion engines: applications, challenges, and global potential. Fuel. 2020;279: 118516.

Awad OI, Mamat R, Ibrahim TK, Kettner M, Kadirgama K, Leman AM, et al. Effects of fusel oil water content reduction on fuel properties, performance and emissions of SI engine fueled with gasoline -fusel oil blends. Renew Energy. 2018;118:858–69.

Şimşek S, Saygın H, Özdalyan B. Improvement of fusel oil features and effect of its use in different compression ratios for an SI engine on performance and emission. Energies (Basel). 2020;13:1824.

Behnke L, Monroe E, Nguyen B, Landera A, George A, Yang Z, et al. Maximizing net fuel economy improvement from fusel alcohol blends in gasoline using multivariate optimization. Fuel Commun. 2022;11: 100059.

Sanni SE, Oni BA. Advances in the Use of Ethers and Alcohols as Additives for Improving Biofuel Properties for SI Engines. Potential and Challenges of Low Carbon Fuels for Sustainable Transport, 2022;153–82.

de Cássia Franco Visioli P, Carolina Capellini M, Gonçalves D, Rodrigues CEC. Higher alcohols as cosolvents of mixtures of ethanol and soybean oil: Solubility and physical properties of ternary systems. Fuel. 2024;358: 130114.

Palani T, Esakkimuthu GS, Dhamodaran G, Seetharaman S. Experimental study on dual oxygenates (ethanol, n-butanol) with gasoline on MPFI engine performance and emission characteristics. Int J Environ Sci Technol. 2024;21:245–54.

Liu L, Peng Y, Zhang W, Ma X. Concept of rapid and controllable combustion for high power-density diesel engines. Energy Convers Manag. 2023;276: 116529.

Abdullah M, Yusop A, Mamat R, Hamidi M, Sudhakar K, Yusaf T. Sustainable biofuels from first three alcohol families: a critical review. Energ (Basel). 2023;16:648.

CAS   Google Scholar  

Cotroneo-Figueroa VP, Gajardo-Parra NF, López-Porfiri P, Leiva Á, Gonzalez-Miquel M, Garrido JM, et al. Hydrogen bond donor and alcohol chain length effect on the physicochemical properties of choline chloride based deep eutectic solvents mixed with alcohols. J Mol Liq. 2022;345: 116986.

Shenbagamuthuraman V, Patel A, Khanna S, Banerjee E, Parekh S, Karthick C, et al. State of art of valorising of diverse potential feedstocks for the production of alcohols and ethers: current changes and perspectives. Chemosphere. 2022;286: 131587.

Muthuraman VS, Kasianantham N. Valorization opportunities and adaptability assessment of algae based biofuels for futuristic sustainability-a review. Process Saf Environ Prot. 2023;174:694–721.

Örs İ, Sayın Kul B, Ciniviz M. A comparative study of ethanol and methanol addition effects on engine performance, combustion and emissions in the SI engine. International Journal of Automotive Science And Technology [Internet]. 2020. [cited 2022 Nov 2]4:[59–69p]. Available from: https://dergipark.org.tr/en/pub/ijastech/issue/53507/713682

Yanju W, Shenghua L, Hongsong L, Rui Y, Jie L, Ying W. Effects of methanol/gasoline blends on a spark ignition engine performance and emissions. Energy and Fuels [Internet]. 2008. [cited 2022 Nov 2]22:[1254–9p]. Available from: https://pubs.acs.org/doi/abs/ https://doi.org/10.1021/ef7003706

Farkade HS, Pathre A. Experimental investigation of methanol, ethanol and butanol blends with gasoline on SI engine. Int J Emerg Technol Adv Eng. 2012;2(4):205–15.

Google Scholar  

Bilgin A, Sezer I. Effects of methanol addition to gasoline on the performance and fuel cost of a spark ignition engine. Energy and Fuels [Internet]. 2008 [cited 2022 Dec 12]:22[2782–8]. Available from: https://pubs.acs.org/doi/abs/ https://doi.org/10.1021/ef8001026

Agarwal AK, Shukla PC, Gupta JG, Patel C, Prasad RK, Sharma N. Unregulated emissions from a gasohol (E5, E15, M5, and M15) fuelled spark ignition engine. Appl Energy. 2015;154:732–41.

Zhao L, Wang D, Qi W. Particulate matter (PM) emissions and performance of bio-butanol-methanol-gasoline blends coupled with air dilution in SI engines. J Aerosol Sci. 2020;145:105546.

Varol Y, Öner C, Öztop HF, Altun Ş. Comparison of methanol, ethanol, or n-butanol blending with unleaded gasoline on exhaust emissions of an SI Engine [Internet]. 2014 [cited 2022 Dec 12]:36[938–48]. Available from: https://www.tandfonline.com/doi/abs/ https://doi.org/10.1080/15567036.2011.572141

Varol Y, Öner C, Öztop HF, Altun Ş. Comparison of Methanol, Ethanol, or n -Butanol Blending with Unleaded Gasoline on Exhaust Emissions of an SI Engine. Energy Sour, Part A: Recovery, Utilization, Environ Eff. 2014;36:938–48.

Mallikarjun M v, Mamilla VR. Experimental Study of Exhaust Emissions & Performance Analysis of Multi Cylinder S.I. Engine When Methanol Used as an Additive [Internet]. 2009. International Journal of Electronic Engineering Research. Available from: http://www.ripublication.com/ijeer.htm

Murali Krishna MVS, Kishor K, Gupta AVSSKS, Murthy PVK, Narasimha KS. Performance of copper coated two stroke spark ignition engine with methanol-blended gasoline with catalytic converter. J Renew Sustain Energy. 2012;4:013102.

Rifal M, Sinaga N. Impact of methanol-gasoline fuel blend on the fuel consumption and exhaust emission of a SI engine. AIP Conf Proc [Internet]. 2016. [cited 2022 Dec 12]:[1725:020070]. Available from: https://aip.scitation.org/doi/abs/ https://doi.org/10.1063/1.4945524

Çelik MB, Özdalyan B, Alkan F. The use of pure methanol as fuel at high compression ratio in a single cylinder gasoline engine. Fuel. 2011;90:1591–8.

Elfasakhany A, Mahrous AF. Performance and emissions assessment of n-butanol–methanol–gasoline blends as a fuel in spark-ignition engines. Alex Eng J. 2016;55:3015–24.

Agarwal J, Alam M, Jaiswal A, Yadav K, Kumar N. Comparative study of emissions and performance of hydrogen boosted si engine powered by gasoline-methanol blend and gasoline-ethanol blend. SAE Technical Papers [Internet]. 2016. [cited 2022 Dec 12] Available from: https://www.sae.org/publications/technical-papers/content/2016-01-1281/

Iliev S. A Comparison of Ethanol, Methanol, and Butanol Blending with Gasoline and Its Effect on Engine Performance and Emissions Using Engine Simulation. Processes 2021, Vol 9, Page 1322 [Internet]. 2021. [cited 2022 Nov 3]:9:[1322p.]. Available from: https://www.mdpi.com/2227-9717/9/8/1322/htm

Altun Ş, Öztop H, Öner C, Varol Y. Exhaust emissions of methanol and ethanol-unleaded gasoline blends in a spark-ignition engine. Therm Sci. 2013;17:291–7.

Sathish Kumar T, Ashok B. Development of combustion control map for flex fuel operation in methanol powered direct injection SI engine. Energy. 2024;288: 129695.

Yang S, Feng J, Sun P, Wang Y, Dong W, Yu X, et al. Combustion and emissions characteristics of methanol/gasoline CISI engines under different injection modes. Fuel. 2023;333: 126506.

Duan X, Feng L, Liu H, Jiang P, Chen C, Sun Z. Experimental investigation on exhaust emissions of a heavy-duty vehicle powered by a methanol-fuelled spark ignition engine under world harmonized transient cycle and actual on-road driving conditions. Energy. 2023;282: 128869.

Zhen X, Wang Y. An overview of methanol as an internal combustion engine fuel. Renew Sustain Energy Rev. 2015;52:477–93.

Shi J, Zhang H, Huang X, Wen J, Chen G, Chen G, et al. Experimental and numerical study of gas explosion from semi-submersible platform. Ocean Eng. 2024;295: 116958.

Balki MK, Sayin C, Canakci M. The effect of different alcohol fuels on the performance, emission and combustion characteristics of a gasoline engine. Fuel. 2014;115:901–6.

Özcan H, ÇAKMAK A. Comparative exergy analysis of fuel additives containing oxygen and hc based in a spark-ignition (SI) engine. International Journal of Automotive Engineering and Technologies [Internet]. 2018 [cited 2022 Nov 2]7:[124–33p.]. Available from: https://dergipark.org.tr/tr/pub/ijaet/issue/40534/486410

Canakci M, Ozsezen AN, Alptekin E, Eyidogan M. Impact of alcohol–gasoline fuel blends on the exhaust emission of an SI engine. Renew Energy. 2013;52:111–7.

Eyidogan M, Ozsezen AN, Canakci M, Turkcan A. Impact of alcohol–gasoline fuel blends on the performance and combustion characteristics of an SI engine. Fuel. 2010;89:2713–20.

Nuthan Prasad BS, Pandey JK, Kumar GN. Impact of changing compression ratio on engine characteristics of an SI engine fueled with equi-volume blend of methanol and gasoline. Energy. 2020;191: 116605.

Elfasakhany A. Investigations on the effects of ethanol–methanol–gasoline blends in a spark-ignition engine: performance and emissions analysis. Eng Sci Technol Int J. 2015;18:713–9.

Chen Z, Wang L, Yuan X, Duan Q, Yang B, Zeng K. Experimental investigation on performance and combustion characteristics of spark-ignition dual-fuel engine fueled with methanol/natural gas. Appl Therm Eng. 2019;150:164–74.

Hou Y, Cheng M, Sheng Z, Wang J. Unsteady conjugate heat transfer simulation of wall heat loads for rotating detonation combustor. Int J Heat Mass Transf. 2024;221: 125081.

Wang Z, Liu H, Long Y, Wang J, He X. Comparative study on alcohols–gasoline and gasoline–alcohols dual-fuel spark ignition (DFSI) combustion for high load extension and high fuel efficiency. Energy. 2015;82:395–405.

Chen Z, Wang L, Zhang Q, Zhang X, Yang B, Zeng K. Effects of spark timing and methanol addition on combustion characteristics and emissions of dual-fuel engine fuelled with natural gas and methanol under lean-burn condition. Energy Convers Manag. 2019;181:519–27.

Liu H, Wang Z, Wang J. Methanol-gasoline DFSI (dual-fuel spark ignition) combustion with dual-injection for engine knock suppression. Energy. 2014;73:686–93.

Huang Z, Lyu Z, Luo P, Zhang G, Ying W, Chen A, et al. Effects of methanol-ammonia blending ratio on performance and emission characteristics of a compression ignition engine. J Mar Sci Eng. 2023;11:2388.

Vancoillie J, Demuynck J, Sileghem L, van de Ginste M, Verhelst S, Brabant L, et al. The potential of methanol as a fuel for flex-fuel and dedicated spark-ignition engines. Appl Energy. 2013;102:140–9.

Daniel R, Wang C, Xu H, Tian G, Richardson D. Dual-injection as a knock mitigation strategy using pure ethanol and methanol. SAE Int J Fuels Lubr. 2012;5:772–84.

Gong C, Liu F, Sun J, Wang K. Effect of compression ratio on performance and emissions of a stratified-charge DISI (direct injection spark ignition) methanol engine. Energy. 2016;96:166–75.

Balki MK, Sayin C. The effect of compression ratio on the performance, emissions and combustion of an SI (spark ignition) engine fueled with pure ethanol, methanol and unleaded gasoline. Energy. 2014;71:194–201.

Li J, Gong CM, Su Y, Dou HL, Liu XJ. Effect of injection and ignition timings on performance and emissions from a spark-ignition engine fueled with methanol. Fuel. 2010;89:3919–25.

Fei M, Zhang Z, Zhao W, Zhang P, Xing Z. Optimal power distribution control in modular power architecture using hydraulic free piston engines. Appl Energy. 2024;358: 122540.

Ghazikhani M, Hatami M, Safari B, Ganji DD. Experimental investigation of performance improving and emissions reducing in a two stroke SI engine by using ethanol additives. Propulsion and Power Research. 2013;2:276–83.

Doğan B, Erol D, Yaman H, Kodanli E. The effect of ethanol-gasoline blends on performance and exhaust emissions of a spark ignition engine through exergy analysis. Appl Therm Eng. 2017;120:433–43.

Mourad M, Mahmoud K. Investigation into SI engine performance characteristics and emissions fuelled with ethanol/butanol-gasoline blends. Renew Energy. 2019;143:762–71.

Zhen X, Li X, Wang Y, Liu D, Tian Z. Comparative study on combustion and emission characteristics of methanol/hydrogen, ethanol/hydrogen and methane/hydrogen blends in high compression ratio SI engine. Fuel. 2020;267: 117193.

Chansauria P, Mandloi RK. Effects of ethanol blends on performance of spark ignition engine-a review. Mater Today Proc. 2018;5:4066–77.

Al-Hasan M. Effect of ethanol–unleaded gasoline blends on engine performance and exhaust emission. Energy Convers Manag. 2003;44:1547–61.

Balaji D, Govindarajan P. Influence of isobutanol blend in spark ignition engine performance and emissions operated with gasoline and ethanol. Int J Eng Sci Technol. 2010;2:2859–68.

Edwin Geo V, Jesu Godwin D, Thiyagarajan S, Saravanan CG, Aloui F. Effect of higher and lower order alcohol blending with gasoline on performance, emission and combustion characteristics of SI engine. Fuel. 2019;256: 115806.

Shirazi SA, Abdollahipoor B, Windom B, Reardon KF, Foust TD. Effects of blending C3–C4 alcohols on motor gasoline properties and performance of spark ignition engines: a review. Fuel Process Technol. 2020;197: 106194.

Turner JWG, Lewis AGJ, Akehurst S, Brace CJ, Verhelst S, Vancoillie J, et al. Alcohol fuels for spark-ignition engines: performance, efficiency, and emission effects at mid to high blend rates for ternary mixtures. Energies (Basel). 2020;13:6390.

Qadiri U, Wani M. Experimental investigation on multi-cylinder SI engine fueled conventional gasoline, ethanol blends, and micro-emulsion as an alternative fuel. Mathematical Modelling of Engineering Problems. 2019;6:69–76.

Masum BM, Masjuki HH, Kalam MA, Palash SM, Habibullah M. Effect of alcohol–gasoline blends optimization on fuel properties, performance and emissions of a SI engine. J Clean Prod. 2015;86:230–7.

Shahad HAK, Wabdan SK. Effect of operating conditions on pollutants concentration emitted from a spark ignition engine fueled with gasoline bioethanol blends. J Renew Energy. 2015;2015:1–7.

Brusstar M, Bakenhus M. Economical, high-efficiency engine technologies for alcohol fuels.

Kumbhar VS, Mali DG, Pandhare PH, Mane RM, Mane & RM. EFFECT OF LOWER ethanol gasoline blends on performance and emission characteristics of the single cylinder SI engine. International Journal of Instrumentation Control and Automation [Internet]. 2020. [cited 2022 Nov 2]1:[10]. Available from: https://www.interscience.in/ijica/vol1/iss4/10

Srinivasan C, Saravanan C. Emission reduction in SI engine using ethanol - gasoline blends on thermal barrier coated pistons. Int J Energy environ. 2010;1(1):715–26.

Wigg B, Coverdill R, Lee C-F, Kyritsis D. Emissions characteristics of neat butanol fuel using a port fuel-injected Spark-Ignition Engine. SAE Tech Pap. 2011. https://doi.org/10.4271/2011-01-0902 .

Pikonas A, Pukalskas S, Grabys J. Influence of composition of gasoline - ethanol blends on parameters of internal combustion engines. Journal of KONES [Internet]. 2003. [cited 2022 Dec 12]:[205–11p.]. Available from: https://www.infona.pl//resource/bwmeta1.element.baztech-article-BUJ6-0026-0027

de Simio L, Gambino M, Iannaccone S. Effect of ethanol content on thermal efficiency of a spark-ignition light-duty engine. ISRN Renew Energy. 2012;2012:1–8.

Kareddula VK, Puli RK. Influence of plastic oil with ethanol gasoline blending on multi cylinder spark ignition engine. Alex Eng J. 2018;57:2585–9.

Nithyanandan K, Zhang J, Li Y, Wu H, Lee TH, Lin Y, et al. Improved SI engine efficiency using acetone–butanol–ethanol (ABE). Fuel. 2016;174:333–43.

Duan X, Li Y, Liu Y, Liu J, Wang S, Guo G. Quantitative investigation the influences of the injection timing under single and double injection strategies on performance, combustion and emissions characteristics of a GDI SI engine fueled with gasoline/ethanol blend. Fuel. 2020;260: 116363.

İlhak Mİ, Doğan R, Akansu SO, Kahraman N. Experimental study on an SI engine fueled by gasoline, ethanol and acetylene at partial loads. Fuel. 2020;261: 116148.

Tian Z, Zhen X, Wang Y, Liu D, Li X. Comparative study on combustion and emission characteristics of methanol, ethanol and butanol fuel in TISI engine. Fuel. 2020;259: 116199.

Suresh D, Porpatham E. Influence of high compression ratio on the performance of ethanol-gasoline fuelled lean burn spark ignition engine at part throttle condition. Case Stud Therm Eng. 2024;53: 103832.

Di Iorio S, Catapano F, Magno A, Sementa P, Vaglieco BM. The potential of ethanol/methanol blends as renewable fuels for DI SI engines. Energies (Basel). 2023;16:2791.

Liu S, Lin Z, Zhang H, Fan Q, Lei N, Wang Z. Experimental study on combustion and emission characteristics of ethanol-gasoline blends in a high compression ratio SI engine. Energy. 2023;274: 127398.

Usman M, Ijaz Malik MA, Chaudhary TN, Riaz F, Raza S, Abubakar M, et al. Comparative assessment of ethanol and methanol-ethanol blends with gasoline in SI engine for sustainable development. Sustainability. 2023;15:7601.

Kalwar A, Singh AP, Agarwal AK. Utilization of primary alcohols in dual-fuel injection mode in a gasoline direct injection engine. Fuel. 2020;276: 118068.

Tornatore C, Marchitto L, Costagliola MA, Valentino G. Experimental comparative study on performance and emissions of E85 adopting different injection approaches in a turbocharged PFI SI engine. Energies (Basel). 2019;12:1555.

Kang R, Zhou L, Hua J, Feng D, Wei H, Chen R. Experimental investigation on combustion characteristics in dual-fuel dual-injection engine. Energy Convers Manag. 2019;181:15–25.

Catapano F, di Iorio S, Sementa P, Vaglieco BM. Investigation of ethanol-gasoline dual fuel combustion on the performance and exhaust emissions of a small SI engine. SAE Technical Papers 2014.

Liu H, Wang Z, Long Y, Wang J. Dual-fuel spark ignition (DFSI) combustion fuelled with different alcohols and gasoline for fuel efficiency. Fuel. 2015;157:255–60.

Catapano F, di Iorio S, Luise L, Sementa P, Vaglieco BM. Influence of ethanol blended and dual fueled with gasoline on soot formation and particulate matter emissions in a small displacement spark ignition engine. Fuel. 2019;245:253–62.

Chen Z, Wang L, Zeng K. A comparative study on the combustion and emissions of dual-fuel engine fueled with natural gas/methanol, natural gas/ethanol, and natural gas/n-butanol. Energy Convers Manag. 2019;192:11–9.

Qian Y, Liu G, Guo J, Zhang Y, Zhu L, Lu X. Engine performance and octane on demand studies of a dual fuel spark ignition engine with ethanol/gasoline surrogates as fuel. Energy Convers Manag. 2019;183:296–306.

Yousufuddin S, Mehdi SN. Exhaust emission analysis of an internal combustion engine fuelled with hydrogen-ethanol dual fuel [Internet]. IJE Transactions B: Applications. 2008. Available from: www.SID.ir

Liu Z, Sun P, Du Y, Yu X, Dong W, Zhou J. Improvement of combustion and emission by combined combustion of ethanol premix and gasoline direct injection in SI engine. Fuel. 2021;292: 120403.

Al-Baghdadi M, Kadhim Alkhabbaz Y, Zeiny A. Effect of ethanol-gasoline blends on exhaust and noise emissions from 4-stroke S. I. engine (Language: English) energy and exergy analysis of Al-Najaf gas turbine power plant. View project Applications of AI and IoT for Renewable and Sustainable Design and Technologies View project [Internet]. Available from: https://www.researchgate.net/publication/282877799

Sileghem, Louis, Casier B;, Coppens A;, Vancoillie J;, Verhelst S; Influence of water content in ethanol-water blends on the performance and emissions of an SI engine. Fisita World Automotive Congress, Proceedings [Internet]. 2014 [cited 2022 Nov 3]: Available from: http://hdl.handle.net/1854/LU-5694558

de Melo TCC, MacHado GB, Belchior CRP, Colaço MJ, Barros JEM, de Oliveira EJ, et al. Hydrous ethanol–gasoline blends – combustion and emission investigations on a flex-fuel engine. Fuel. 2012;97:796–804.

Takamura P, Guilherme R. Performance potential of an ethanol fueled turbocharged direct injection otto engine. SAE Technical Papers [Internet]. 2012. [cited 2022 Nov 3]: Available from: https://www.sae.org/publications/technical-papers/content/2012-36-0508/

Brewster S, Railton D, Maisey M, Frew R. The effect of E100 water content on high load performance of a spray guide direct injection boosted engine. SAE Technical Papers [Internet]. 2007. [cited 2022 Nov 3]: Available from: https://www.sae.org/publications/technical-papers/content/2007-01-2648/

Boretti A. Performances of a turbocharged E100 engine with direct injection and variable valve actuation. SAE Technical Papers [Internet]. 2010. [cited 2022 Nov 3]: Available from: https://www.sae.org/publications/technical-papers/content/2010-01-2154/

Phuangwongtrakul S, Wechsatol W, Sethaput T, Suktang K, Wongwises S. Experimental study on sparking ignition engine performance for optimal mixing ratio of ethanol–gasoline blended fuels. Appl Therm Eng. 2016;100:869–79.

Yousufuddin S, Mehdi SN, Masood M. Performance and combustion characteristics of a hydrogen−ethanol-fuelled engine. Energy and Fuels [Internet]. 2008. [cited 2022 Nov 3] 22: [3355–62]. Available from: https://pubs.acs.org/doi/abs/ https://doi.org/10.1021/ef800309b

Yousufuddin S, Masood M. Effect of ignition timing and compression ratio on the performance of a hydrogen–ethanol fuelled engine. Int J Hydrogen Energy. 2009;34:6945–50.

Li L, Liu Z, Wang H, Deng B, Xiao Z, Wang Z, et al. Combustion and emissions of ethanol fuel (E100) in a small SI engine. SAE Technical Papers [Internet]. 2003. [cited 2022 Nov 3]: Available from: https://www.sae.org/publications/technical-papers/content/2003-01-3262/

Zuo Q, Zhu X, Liu Z, Zhang J, Wu G, Li Y. Prediction of the performance and emissions of a spark ignition engine fueled with butanol-gasoline blends based on support vector regression. Environ Prog Sustain Energy. 2019;38: e13042.

Liu K, Li Y, Yang J, Deng B, Feng R, Huang Y. Comprehensive study of key operating parameters on combustion characteristics of butanol-gasoline blends in a high speed SI engine. Appl Energy. 2018;212:13–32.

Jesu Godwin D, Edwin Geo V, Thiyagarajan S, Leenus Jesu Martin M, Maiyalagan T, Saravanan CG, et al. Effect of hydroxyl (OH) group position in alcohol on performance, emission and combustion characteristics of SI engine. Energy Convers Manag. 2019;189:195–201.

Erdiwansyah MR, Sani MSM, Sudhakar K, Kadarohman A, Sardjono RE. An overview of higher alcohol and biodiesel as alternative fuels in engines. Energy Rep. 2019;5:467–79.

Sayin C, Balki MK. Effect of compression ratio on the emission, performance and combustion characteristics of a gasoline engine fueled with iso-butanol/gasoline blends. Energy. 2015;82:550–5.

Singh SB, Dhar A, Agarwal AK. Technical feasibility study of butanol–gasoline blends for powering medium-duty transportation spark ignition engine. Renew Energy. 2015;76:706–16.

Li L, Wang T, Duan J, Sun K. Impact of butanol isomers and EGR on the combustion characteristics and emissions of a SIDI engine at various injection timings. Appl Therm Eng. 2019;151:417–30.

Nithyanandan K, Wu H, Huo M, Lee C-F. A Preliminary Investigation of the Performance and Emissions of a Port-Fuel Injected SI Engine Fueled with Acetone-Butanol-Ethanol (ABE) and Gasoline. SAE Technical Paper. 2014.

Mittal N, Athony RL, Bansal R, Ramesh KC. Study of performance and emission characteristics of a partially coated LHR SI engine blended with n-butanol and gasoline. Alex Eng J. 2013;52:285–93.

Tian Z, Zhen X, Wang Y, Liu D, Li X. Combustion and emission characteristics of n-butanol-gasoline blends in SI direct injection gasoline engine. Renew Energy. 2020;146:267–79.

Siwale L, Kristóf L, Bereczky A, Mbarawa M, Kolesnikov A. Performance, combustion and emission characteristics of n-butanol additive in methanol–gasoline blend fired in a naturally-aspirated spark ignition engine. Fuel Process Technol. 2014;118:318–26.

Elfasakhany A. Experimental investigation on SI engine using gasoline and a hybrid iso-butanol/gasoline fuel. Energy Convers Manag. 2015;95:398–405.

Xiaolong Y, Jing Y, Tieping L. The effect of an SI engine using butanol–gasoline blended fuel on performance and environment. In 2009 International Conference on Energy and Environment Technology. IEEE. 2009. p. 402–5.

Liu Z, Zhen X, Tian Z, Liu D, Wang Y. Study on the effect of injection strategy on the combustion and emission characteristics of direct injection spark ignition bio-butanol engine. Energy. 2024;289: 129958.

Yu X, Wang T, Guo Z, Zhao Z, Li D, Li Y, et al. Effect of exhaust gas recirculation (EGR) on combustion and emission of butanol/gasoline combined injection engine. Energy. 2024;295: 130940.

Liu Z, Zhen X, Geng J, Tian Z. Effects of injection timing on mixture formation, combustion, and emission characteristics in a n-butanol direct injection spark ignition engine. Energy. 2024;295: 131059.

Shang Z, Yu X, Ren L, Li Z, Wang H, Li Y, et al. Synergic effect of hydrogen and n-butanol on combustion and emission characteristics of a hydrogen direct injection SI engine fueled with n-butanol/gasoline. Int J Hydrogen Energy. 2024;49:945–56.

Galloni E, Fontana G, Scala F. Experimental and numerical analyses of a spark-ignition engine firing with n-butanol-gasoline blends at high load operation. Energy Procedia. 2018;148:336–43.

Tang Q, Jiang P, Peng C, Chang H, Zhao Z. Comparison and analysis of the effects of spark timing and lambda on a high-speed spark ignition engine fuelled with n-butanol/gasoline blends. Fuel. 2021;287: 119505.

Ashok B, Saravanan B, Nanthagopal K, Azad AK. Investigation on the effect of butanol isomers with gasoline on spark ignition engine characteristics. Advanced Biofuels: Applications, Technologies and Environmental Sustainability. 2019. P. 265–89.

Saraswat M, Chauhan NR. Comparative assessment of butanol and algae oil as alternate fuel for SI engines. Eng Sci Technol Int J. 2020;23:92–100.

Guo Z, Yu X, Dong W, Sun P, Shi W, Du Y, et al. Research on the combustion and emissions of an SI engine with acetone-butanol-ethanol (ABE) port injection plus gasoline direct injection. Fuel. 2020;267: 117311.

Shang W, Yu X, Shi W, Xing X, Guo Z, Du Y, et al. Effect of exhaust gas recirculation and hydrogen direct injection on combustion and emission characteristics of a n-butanol SI engine. Int J Hydrogen Energy. 2020;45:17961–74.

Dhamodaran G, Esakkimuthu GS, Palani T, Krishnan R. feasibility of adding fusel oil as an oxygenate to gasoline on reducing MPFI engine emissions. Environ Eng Manag J. 2022;21:1255–64.

Awad OI, Mamat R, Ali OM, Azmi WH, Kadirgama K, Yusri IM, et al. Response surface methodology (RSM) based multi-objective optimization of fusel oil -gasoline blends at different water content in SI engine. Energy Convers Manag. 2017;150:222–41.

Calam A, Solmaz H, Uyumaz A, Polat S, Yilmaz E, Içingür Y. Investigation of usability of the fusel oil in a single cylinder spark ignition engine. J Energy Inst. 2015;88:258–65.

Solmaz H. Combustion, performance and emission characteristics of fusel oil in a spark ignition engine. Fuel Process Technol. 2015;133:20–8.

Calam A, İçingür Y, Solmaz H, Yamık H. A comparison of engine performance and the emission of fusel oil and gasoline mixtures at different ignition timings. Int J Green Energy. 2015;12:767–72.

Maciej Serda, Becker FG, Cleary M, Team RM, Holtermann H, The D, et al. THE EFFECTS OF THE BLENDS OF FUSEL OIL AND GASOLINE ON PERFORMANCE AND EMISSIONS IN A SPARK IGNITION ENGINE. G. Balint, Antala B, Carty C, Mabieme J-MA, Amar IB, Kaplanova A, editors. Journal of the faculty of engineering and architecture of Gazi university [Internet]. 2012. [cited 2022 Dec 12]27:[343–54 p.]. Available from: https://avesis.gazi.edu.tr/yayin/1406eb65-4ace-4902-b5ec-e48fd661643d/the-effects-of-the-blends-of-fusel-oil-and-gasoline-on-performance-and-emissions-in-a-spark-ignition-engine

Awad OI, Ali OM, Hammid AT, Mamat R. Impact of fusel oil moisture reduction on the fuel properties and combustion characteristics of SI engine fueled with gasoline-fusel oil blends. Renew Energy. 2018;123:79–91.

Simsek S, Ozdalyan B. Improvements to the composition of fusel oil and analysis of the effects of fusel oil-gasoline blends on a spark-ignited (SI) engine’s performance and emissions. Energies (Basel). 2018;11:625.

Abdalla AN, Tao H, Bagaber SA, Ali OM, Kamil M, Ma X, et al. Prediction of emissions and performance of a gasoline engine running with fusel oil–gasoline blends using response surface methodology. Fuel. 2019;253:1–14.

Abdalla AN, Awad OI, Tao H, Ibrahim TK, Mamat R, Hammid AT. Performance and emissions of gasoline blended with fusel oil that a potential using as an octane enhancer. Energy Sour, Part A: Recovery, Utilization Environ Eff. 2019;41:931–47.

Rosdia SM, Mamata R, Azri A, Sudhakar K, Yusri IM. Evaluation of properties on performance and emission to turbocharged SI engine using fusel oil blend with gasoline. IOP Conf Ser Mater Sci Eng. 2019;469: 012113.

Simsek S, Uslu S. Experimental study of the performance and emissions characteristics of fusel oil/gasoline blends in spark ignited engine using response surface methodology. Fuel. 2020;277: 118182.

Liu L, Mei Q, Jia W. A flexible diesel spray model for advanced injection strategy. Fuel. 2022;314: 122784.

Safieddin Ardebili SM, Solmaz H, Mostafaei M. Optimization of fusel oil – Gasoline blend ratio to enhance the performance and reduce emissions. Appl Therm Eng. 2019;148:1334–45.

Kocakulak T, Babagiray M, Nacak Ç, Safieddin Ardebili SM, Calam A, Solmaz H. Multi objective optimization of HCCI combustion fuelled with fusel oil and n-heptane blends. Renew Energy. 2022;182:827–41.

Turner D, Xu H, Cracknell RF, Natarajan V, Chen X. Combustion performance of bio-ethanol at various blend ratios in a gasoline direct injection engine. Fuel. 2011;90:1999–2006.

Muthuraman VS, Patel A, Shreya V, Vaidyanathan A, Reshwanth KNGL, Karthick C, et al. Progress on compatibility issues of alcohols on automotive materials: Kinetics, challenges and future prospects- a comprehensive review. Process Saf Environ Prot. 2022;162:463–93.

Aleiferis PG, van Romunde ZR. An analysis of spray development with iso-octane, n-pentane, gasoline, ethanol and n-butanol from a multi-hole injector under hot fuel conditions. Fuel. 2013;105:143–68.

Feng Z, Zhan C, Tang C, Yang K, Huang Z. Experimental investigation on spray and atomization characteristics of diesel/gasoline/ethanol blends in high pressure common rail injection system. Energy. 2016;112:549–61.

Lapuerta M, Rodríguez-Fernández J, Fernández-Rodríguez D, Patiño-Camino R. Cold flow and filterability properties of n-butanol and ethanol blends with diesel and biodiesel fuels. Fuel. 2018;224:552–9.

Kumar TS, Ashok B. Critical review on combustion phenomena of low carbon alcohols in SI engine with its challenges and future directions. Renew Sustain Energy Rev. 2021;152: 111702.

Yu X, Li D, Yang S, Sun P, Guo Z, Yang H, et al. Effects of hydrogen direct injection on combustion and emission characteristics of a hydrogen/Acetone-Butanol-Ethanol dual-fuel spark ignition engine under lean. Elsevier [Internet]. [cited 2022 Nov 3]: Available from: https://www.sciencedirect.com/science/article/pii/S036031992033500X

Zhao L, Wang D, Qi W. Comparative study on air dilution and hydrogen-enriched air dilution employed in a SI engine fueled with iso-butanol-gasoline. Int J Hydrog Energy. 2020;45:10895–905.

Shang Z, Yu X, Shi W, Huang S, Li G, Guo Z, et al. Numerical research on effect of hydrogen blending fractions on idling performance of an n-butanol ignition engine with hydrogen direct injection. Fuel. 2019;258: 116082.

Najafi G, Ghobadian B, Yusaf T, Safieddin Ardebili SM, Mamat R. Optimization of performance and exhaust emission parameters of a SI (spark ignition) engine with gasoline–ethanol blended fuels using response surface methodology. Energy. 2015;90:1815–29.

Malik MAI, Usman M, Waqas Rafique M, Raza S, Saleem MW, Abbas N, et al. Managing energy transition alongside environmental protection by making use of AI-led butanol powered SI engine optimization in compliance with SDGs. Heliyon. 2024;10: e29698.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Jambo SA, Abdulla R, Marbawi H, Gansau JA. Response surface optimization of bioethanol production from third generation feedstock - eucheuma cottonii. Renew Energy. 2019;132:1–10.

Yousaf M, Mahmood A, Elkamel A, Rizwan M, Zaman M. Techno-economic analysis of integrated hydrogen and methanol production process by CO2 hydrogenation. Int J Greenhouse Gas Control. 2022;115: 103615.

Zheng Y, Ngo HH, Luo H, Wang R, Li C, Zhang C, et al. Production of cost-competitive bioethanol and value-added co-products from distillers’ grains: techno-economic evaluation and environmental impact analysis. Bioresour Technol. 2024;397: 130470.

Jeya G, Dhanalakshmi R, Anbarasu M, Vinitha V, Sivamurugan V. Techno-economic analysis of butanol biosynthesis. Biomass, Biofuels, Biochemicals, Elsevier. 2022. [75–94P.].

Karthick C, Nanthagopal K. A comprehensive review on ecological approaches of waste to wealth strategies for production of sustainable biobutanol and its suitability in automotive applications. Energy Convers Manag. 2021;239: 114219.

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Acknowledgements

The authors gratefully acknowledge the financial support offered by the British Council under the Goin Global Industry Academia Partnership Grant 2022 [Project ID: IND/CONT/G/23-24/A2(G/22-23/19) to carry out this project. The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP 2/173/45.

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Nuclear energy reviews and inquiries: a quick guide

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Introduction.

Nuclear-powered electricity production (often called nuclear power or nuclear energy) is currently prohibited in Australia under the  Environment Protection and Biodiversity Conservation Act 1999  and the  Australian Radiation Protection and Nuclear Safety Act 1998 . It is also prohibited by legislation in several states as summarised in the Parliamentary Library’s paper,  Current prohibitions on nuclear activities in Australia: a quick guide . Over the last 20 years, there have been several reviews and inquiries that have considered the removal of some of these prohibitions as well as the feasibility of nuclear energy in Australia.

This quick guide describes, in chronological order, the major government inquiries and reviews into nuclear energy that have occurred at the federal and state levels over this period. It includes inquiries with a substantial focus on nuclear energy in their terms of reference. It does not include those that focus on uranium mining and processing, nuclear waste management or nuclear-powered submarines. Some information on these topics is contained in the Parliamentary Library’s paper,  Radioactive waste management in Australia 2012 – 2022: a chronology  and Bills digest ,  Australian Naval Nuclear Power Safety Bill 2023 [and] Australian Naval Nuclear Power Safety (Transitional Provisions) Bill 2023 .

2005–06 Parliamentary inquiry into developing Australia’s non-fossil fuel energy industry

On 15 March 2005, the then Minister for Industry, Tourism and Resources, Ian Macfarlane, asked the House Standing Committee on Industry and Resources to  inquire into the development of the non-fossil fuel energy industry . The  terms of reference  for the inquiry specify the inquiry is to commence ‘with a case study into the strategic importance of Australia’s uranium resource’.

While nuclear energy forms part of the inquiry, the primary focus of the inquiry is other elements of the nuclear fuel cycle, including uranium mining, processing and export. The 802-page final report,  Australia’s uranium: greenhouse friendly fuel for an energy hungry world , was published in November 2006. This includes Chapter 4 on greenhouse gas emissions and nuclear power.

The report made 14 recommendations, with part of one recommendation directly relating to the prohibitions on nuclear energy, Recommendation 12 (pp. xlii–xliii):

The Committee recommends that the Australian and state governments, through the Council of Australian Governments:

• examine how Australia might seek greater beneficiation of its uranium resources prior to export and encourage such a development, while meeting non-proliferation objectives proposed in initiatives such as the US Global Nuclear Energy Partnership (GNEP) and the International Atomic Energy Agency’s (IAEA) proposed multilateral approaches to the nuclear fuel cycle;
• examine the possible establishment of fuel cycle facilities (for example, uranium conversion and enrichment plants) which, in accordance with the IAEA’s recommendation for such facilities to be operated on a multilateral basis, could be operated on a joint ownership, co-management or drawing rights basis with countries in the region intending to use nuclear energy in the future;
• examine whether, in light of the advances in spent fuel management proposed in the GNEP initiative, there is in fact a potential role for Australia in the back-end of the fuel cycle;
• in the event these proposals are adopted, develop a licensing and regulatory framework, that meets world’s best practice, to provide for the possible establishment of fuel cycle services industries and facilities in Australia; and
• Section 140A of the Environment Protection and Biodiversity Conservation Act 1999, and
• Section 10 of the Australian Radiation Protection and Nuclear Safety Act 1998.

The Coalition government  responded  to the report in March 2007, stating that Recommendation 12 was ‘noted’ and that the government would ‘develop a workplan on options for an appropriate regulatory framework for an expanded nuclear industry’ (pp. 5–6).

2006 Prime ministerial uranium mining, processing and nuclear energy review (Switkowski Review)

On 6 June 2006, then Prime Minister John Howard established the  Prime Ministerial Uranium Mining, Processing and Nuclear Energy Review Taskforce  to undertake a review. This became commonly known as the Switkowski Review, after the chair of the taskforce, Dr Ziggy Switkowski. The  terms of reference  for the review were to consider the economic issues of the nuclear fuel cycle and nuclear energy, environmental issues, and health safety and proliferation issues. The  final report of the taskforce  was published on  29 December 2006 .

The review is notable as the first major review of nuclear energy in Australia since the introduction of the Commonwealth prohibitions, and the final report,  Uranium mining, processing and nuclear energy review: opportunities for Australia? , summarises the key issues of the nuclear debate during 2006. The final report did not make any recommendations but did include several key findings.

The key findings highlight the potential that nuclear energy could play in diversifying Australia’s energy mix, including a potential source of baseload power, and emissions reduction (p. 2). However, the report also highlights some of the limitations and challenges that a nuclear industry would face, including economics, introduction of regulation, waste management, and community acceptance (pp. 2, 5–6).

2015–16 South Australia Nuclear Fuel Cycle Royal Commission

The South Australian Government established the  Nuclear Fuel Cycle Royal Commission  on 19 March 2015, and the  final report  was publicly released on 9 May 2016. The  terms of reference  included consideration of nuclear energy generation, as well as nuclear ore mining and processing, nuclear waste management and other elements of the nuclear fuel cycle.

The royal commission’s final report made 12 recommendations for the South Australian Government, including 3 related to nuclear energy (p. 169):

8. pursue removal at the federal level of existing prohibitions on nuclear power generation to allow it to contribute to a low-carbon electricity system, if required 9. promote and collaborate on the development of a comprehensive national energy policy that enables all technologies, including nuclear, to contribute to a reliable, low-carbon electricity network at the lowest possible system cost 10. collaborate with the Australian Government to commission expert monitoring and reporting on the commercialisation of new nuclear reactor designs that may offer economic value for nuclear power generation

The South Australian Labor Government  responded  to the commission in November 2016. It supported 9 of the 12 recommendations (including Recommendations 9 and 10), but did not support Recommendation 8, noting that ‘nuclear power in the short to medium term is not a cost‑effective source of low-carbon electricity for South Australia and, as such, pursuing the removal of Commonwealth legislative prohibitions cannot be justified’ (p. 15).

2019 Parliamentary inquiry into the prerequisites for nuclear energy in Australia

Following a referral from the then Minister for Energy and Emissions Reduction, Angus Taylor, the House of Representatives Standing Committee on the Environment and Energy resolved to conduct an  inquiry into the prerequisites for nuclear energy in Australia  on 6 August 2019. The  terms of reference  for the inquiry were broad and included all matters related to nuclear energy. The final report,  Not without your approval: a way forward for nuclear technology in Australia , was tabled on 13 December 2019.

The committee made 3 multi-part recommendations in the report. Recommendation 1 includes ‘that the Australian Government consider the prospect of nuclear energy technology as part of its future energy mix’ (p. xi), and Recommendation 3 includes ‘that the Australian Government allow partial and conditional consideration of nuclear energy technology’, by lifting the moratorium on nuclear energy in relation to Generation III+ and IV reactors while keeping the moratorium on earlier generation reactors (p. xiii).

The inquiry also produced 2 dissenting reports, one from Labor MPs (pp. 55–73) and one from Independent MP Zali Steggall (pp. 75–93).

At the time of writing this Quick Guide, a government response has not been tabled.

2019–20 New South Wales inquiry into the Uranium Mining and Nuclear Facilities (Prohibitions) Repeal Bill 2019

The  Uranium Mining and Nuclear Facilities (Prohibitions) Repeal Bill 2019 (NSW) was a Private Member’s Bill introduced by One Nation MLC Mark Latham in the Parliament of NSW on 6 June 2019. The intent of the Bill was to repeal the  Uranium Mining and Nuclear Facilities (Prohibitions) Act 1986 (NSW). The Bill was  referred to the Standing Committee on State Development  on 6 June 2019, and the  final report  was tabled on 4 March 2020.

The committee made 9 recommendations in the report, including (p. xii):

Recommendation 6 – That the NSW Government supports the repeal of the Uranium Mining and Nuclear Facilities (Prohibitions) Act 1986 in its entirety. Recommendation 7 – That the NSW Government pursues the repeal of the Commonwealth prohibitions on nuclear facilities by making representations to the Commonwealth Minister with portfolio responsibility for the relevant legislation. Recommendation 8 – That the Legislative Council proceed with debate on the bill, having regard to the findings and recommendations contained in this report.

The report includes a dissenting statement from Labor MLCs (pp. 149–150).

The Coalition NSW Government issued  a response to the report  on 4 September 2020. It supported or supported in principle all the recommendations in the report except Recommendation 6, which was noted, and Recommendation 8, which was not supported, stating ‘[i]f the NSW Government decides to amend the  Uranium Mining and Nuclear Facilities (Prohibitions) Act 1986 , it will introduce its own legislation to do so’ (p. 4). The Bill failed to progress and lapsed on prorogation on 27 February 2023.

2019–20 Victoria inquiry into nuclear prohibition

On 14 August 2019, the Victorian Legislative Council agreed to a motion for the Environment and Planning Committee to  inquire into the potential benefits to Victoria  in removing prohibitions enacted by the  Nuclear Activities (Prohibitions) Act 1983  (Vic). The final report,  Inquiry into nuclear prohibition , was tabled on 26 November 2020.

The report did not make any recommendations but made 12 key findings, including (pp. 84, 106):

Finding 6 – Discussion about Victorian participation in the nuclear fuel cycle is entirely theoretical while the Commonwealth prohibitions remain in place Finding 7 – Until there is a change in the Commonwealth position, detailed discussions about emerging technologies in Victoria related to the nuclear fuel cycle and power generation are unlikely to advance.

The inquiry produced 3 minority reports; one by Shooters, Farmers and Fishers MLC Jeff Bourman (p. 242 of pdf), one by Liberal and Liberal Democrat MLCs (pp. 243–246 of pdf), and one by Labor MLC Nina Taylor (pp. 247–256 of pdf).

As the committee made no recommendations,  no government response is required .

2022–23 Parliamentary inquiry into the Environment and Other Legislation Amendment (Removing Nuclear Energy Prohibitions) Bill 2022

The  Environment and Other Legislation Amendment (Removing Nuclear Energy Prohibitions) Bill 2022 (Cth) is a Private Senator’s Bill  introduced  on 28 September 2022 by Senator Matthew Canavan, and co-sponsored by 8 other Coalition senators. The intent of the Bill is to amend the  Environment Protection and Biodiversity Conservation Act 1999 and the  Australian Radiation Protection and Nuclear Safety Act 1998  to remove the prohibition on nuclear installations.

The Senate  referred the Bill  to the Environment and Communications Legislation Committee on 27 October 2022, and the committee’s  final report  was tabled on 11 August 2023. The committee recommended that the Bill not be passed (p. 74 of pdf).

Coalition senators provided a dissenting report (pp. 75–95).

The Bill remains before the Senate and, unless it progresses further, will lapse at the prorogation of the current 47th Parliament.

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