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A review of energy management systems and organizational structures of prosumers.

1. Introduction

• EMS based on ESS [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ];
• EMS based on DSM that integrates the most common demand response programs (DRP) [ 32 , 33 , 34 , 35 ];
• hybrid EMS that take into account both (mentioned above) [ 36 , 37 , 38 , 39 , 40 ].
• an EMS with one objective [ 41 , 42 , 43 , 44 ];
• an EMS with multiple objectives [ 24 , 34 , 45 , 46 ].
• Classical mathematical programming methods [ 30 , 48 , 49 ];
• Methods based on an intelligent solution space search (metaheuristic methods) [ 24 , 35 , 50 , 51 , 52 ];
• Rule-based methods (RBA) [ 27 , 30 ];
• Multi-agent systems (MAS) [ 38 , 42 , 53 , 54 , 55 ];
• Artificial intelligence (AI) [ 56 , 57 , 58 ];
• Other approaches [ 59 ];
• Hybrid methods (a combination of several methods).
• Optimization frameworks;
• Methods for predicting electricity generation;
• Methods for predicting electricity consumption;
• Participation in the electricity market.

2. Methodology

• scientific papers published in the last five years were taken into account, with the exception of highly cited papers with a larger scope published more than five years ago that were also taken into account;
• papers dealing with the development of EMS systems for prosumers and microgrids were taken into account;
• papers dealing with the development of EMS based on ESS, DSM, hybrid EMS and EV were considered;
• review papers on the topics of prosumer EMS, microgrid EMS, input data prediction in optimization problems and the electricity market were taken into account, but also published in the last five years, with the exception of highly cited papers;
• fundamental books of high quality with the topic of RES integration and their impact on the grid were considered;
• other aspects, such as security and communication technologies, were not taken into account.
• a quality and comprehensive overview of the research topic, analysis of review papers published so far, as well as identification of room for improvement and the gap planned to be filled by the current research are presented in the introductory part;
• a detailed overview of the prosumer control structure, EMS with a detailed examination of each aspect and the market environment are presented as a result of the conducted research;
• recommendations, the conclusion and room for improvement are based on a detailed review of scientific papers.

3. An Overview of the Prosumer Control Structure

• Primary regulation is realized using a fast local controller in control of only one element of the microgrid, be it DG, a controllable load or several aggregated elements;
• Secondary regulation is usually realized using the central controller in control of coordination and supervision of all local controllers;
• Tertiary regulation serves as an intermediary between the central microgrid controller and external agents such as aggregators, grid operators, or electricity market operators.
• Lower control functions—regulation of voltage, frequency, active and reactive power at the level of local controllers of each controllable element of the microgrid;
• Essential control functions—operation between on-grid and off-grid mode and vice versa, and energy management;
• Upward control functions—realization of communication with the system operator, market operator, and aggregator, and integration into external information and communication systems.

4. An Overview of a Prosumer EMS

• hard constraints—must be satisfied in the solution;
• soft constraints—satisfaction in the solution is not essential but desirable.
• a rule-based algorithm—used for shifting loads to periods of low prices and reducing peak load;
• artificial intelligence—used for finding optimum maintenance of heat, consumption energy, renewable energy use, turning devices on and off, reducing total energy costs using an artificial neural network (ANN), fuzzy logic control (FL) and an adaptive neuro-fuzzy inference system (ANFIS);
• optimization methods (techniques)—the objective function is the minimization of errors, cost, optimal design and management using classical mathematical and heuristic optimization methods (techniques).
• evolutionary computing (EC);
• swarm intelligence (SI).
• human machine interface (HMI) of the operator for monitoring and entering input settings;
• supervisory control and data acquisition (SCADA);
• a module for predicting input data required for optimization based on current and historical measurement data;
• the optimization module responsible for optimal operations by generating decisions for the observed scheduling horizon.
• The type of the prosumer and the elements the prosumer integrates;
• The market environment in which the prosumer is integrated;
• Methods for predicting input parameters in optimization problems;
• Optimization frameworks and optimization problems of the prosumer EMS.
• electricity sources: – controllable sources (CS), – uncontrollable sources (RES);
• electricity loads: – controllable loads (CL), – uncontrollable (critical) loads (UL);
• energy storage systems: – electrochemical systems (secondary batteries), – chemical systems, – electrical systems.

4.1. Types and Elements of the Prosumer

4.1.1. electricity sources.

• generators with an internal combustion engine (usually diesel or petrol) (GWICE) [ 37 , 46 , 96 , 101 , 105 , 111 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 ];
• microturbine (MT) [ 24 , 39 , 46 , 47 , 85 , 92 , 96 , 97 , 103 , 106 , 107 , 108 , 109 , 110 , 112 , 115 , 117 , 119 , 122 , 123 , 124 , 125 ];
• cogeneration power plants for simultaneous production of electricity and heat (CP) [ 25 , 88 , 92 , 96 , 97 , 100 , 110 , 116 , 123 , 125 , 126 , 127 ].
• the minimum/maximum output power of the aggregator (electricity power source);
• the rate of change of the output power or ramp up/down;
• the minimum electricity generation time and the minimum interruption time of electricity generation or the minimum up/down time;
• electricity generation (working time) costs are most often divided into fuel and start-up costs.

4.1.2. Electricity Loads

4.1.3. Energy Storage Technologies

• energy management;
• energy storage.
• electrical storage (ES)—(i) supercapacitor and (ii) superconducting coil;
• mechanical storage (MS)—(i) pump-accumulation hydropower plant, (ii) compressed air, and (iii) flywheels;
• electrochemical storage (ECS)—(i) secondary batteries and (ii) instantaneous batteries;
• thermochemical storage (TCS)—solar fuel;
• chemical storage (CHS)—fuel cells;
• thermal storage (TS)—(i) low-temperature energy storage and (ii) high-temperature energy tank.

4.2. Prediction of Input Parameters in Prosumer Optimization Problems

• statistical methods;
• physical methods;
• artificial intelligence methods;
• hybrid methods,
• very short term (min–h);
• short-term (h–week);
• medium short-term (month–year);
• long-term (over a year).

4.3. Optimization Framework and Optimization Problems of the Prosumer EMS

• optimization framework;
• optimization method;
• objective function and constraints.

4.3.1. Optimization Framework

• 24 h (one day) [ 24 , 25 , 26 , 30 , 32 , 37 , 46 , 47 , 49 , 55 , 58 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 89 , 90 , 91 , 92 , 93 , 94 , 96 , 97 , 98 , 99 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 ];
• 6 h [ 100 , 114 ];
• 12 h [ 39 , 113 , 114 ];
• 20 h [ 88 ];
• 48 h [ 27 , 85 , 114 ];
• 72 h [ 114 ];
• 96 h [ 95 , 114 ];
• 168 h (one week) [ 52 , 66 , 86 , 87 ].

4.3.2. Optimization Methods

• classical mathematical programming methods;
• methods based on intelligent search of solution space (global optimum approximation methods, metaheuristics);
• rule-based methods;
• multi-agent systems;
• hybrid methods.
• linear programming (LP) [ 86 , 92 , 129 ];
• mixed-integer linear programming (MILP) [ 30 , 32 , 39 , 49 , 58 , 66 , 67 , 69 , 71 , 74 , 88 , 91 , 93 , 97 , 100 , 102 , 104 , 105 , 106 , 107 , 108 , 109 , 111 , 112 , 113 , 115 , 116 , 119 , 122 , 123 , 127 , 128 , 129 ];
• quadratic programming (QP) [ 26 , 89 , 90 , 98 , 114 , 119 ];
• mixed-integer quadratic programming (MIQP) [ 85 , 126 ];
• nonlinear programming (NLP) [ 70 , 92 , 94 , 120 , 122 ];
• mixed-integer nonlinear programming (MINLP) [ 68 , 96 , 103 , 112 , 114 , 121 ];
• dynamic programming (DP) [ 73 , 99 ];
• sequential linear programming (SLP) [ 124 ];
• sequential quadratic programming (SQP) [ 110 ];
• semidefinite programming (SDP) [ 101 , 103 ];
• convex mixed-integer second-order cone programming (CMISOCP) [ 117 ];
• hybrid methods of using dynamic programming and linear programming (HDPLP) [ 47 ];
• transformation of mixed-integer nonlinear programming in semidefinite programming (TMINLPSP) [ 103 ];
• other approaches [ 72 ].
• particle swarm optimization (PSO) [ 46 , 95 , 118 , 129 ];
• whale optimization algorithm (WOA) [ 37 ];
• genetic algorithm (GA) [ 125 ];
• most valuable player algorithm (MVPA) [ 25 ];
• hybrid algorithm (combining the genetic algorithm and particle optimization) (HGAPO) [ 24 ];
• hybrid algorithm (the genetic algorithm and fuzzy logic) (HGAFL) [ 52 , 87 ];
• greedy algorithm (GRA) [ 68 ].

4.3.3. Objective Functions and Constraints

• economic objective [ 25 , 27 , 30 , 32 , 37 , 39 , 47 , 49 , 52 , 55 , 58 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 86 , 87 , 88 , 89 , 90 , 91 , 93 , 94 , 95 , 97 , 98 , 99 , 100 , 102 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 116 , 118 , 119 , 121 , 122 , 123 , 124 , 126 , 127 , 128 , 129 ];
• technical objective [ 66 , 85 ];
• a combination of technical and economic objective [ 26 , 96 , 101 , 103 , 114 , 115 , 117 , 120 ];
• a combination of environmental and economic objective [ 24 , 92 , 125 ];
• a combination of all three objectives, i.e. economic, technical and environmental [ 46 ].

5. An Overview of the Prosumer Market Environment

6. recommendations for future work.

• Optimization problems lack detailed models of EVs that encompass different types of energy management during the charging/discharging process and predict their usage patterns.
• EVs, PV systems and ESS are almost always interfaced with power converters that are regularly left out in optimization models.
• For detailed battery models, it is necessary to consider the amount of charging and discharging power, which is not equal in the entire range but depends on various factors and, most notably, on the state of charge of the battery.
• Input data such as RES generation, load, and market prices into optimization models rarely use exact prediction methods.
• Participation of prosumers in new market mechanisms, especially the local market environments, and detailed modeling of DRP must be further developed and improved.
• Optimization frameworks play a very important role in alleviating the uncertainty associated with RES generation, load and market prices that influence the optimality of the solution. High volatility of RES generation and loads demands higher temporal resolution of the optimization time step, especially when participating in emerging electricity markets.

7. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Mišljenović, N.; Žnidarec, M.; Knežević, G.; Šljivac, D.; Sumper, A. A Review of Energy Management Systems and Organizational Structures of Prosumers. Energies 2023 , 16 , 3179. https://doi.org/10.3390/en16073179

Mišljenović N, Žnidarec M, Knežević G, Šljivac D, Sumper A. A Review of Energy Management Systems and Organizational Structures of Prosumers. Energies . 2023; 16(7):3179. https://doi.org/10.3390/en16073179

Mišljenović, Nemanja, Matej Žnidarec, Goran Knežević, Damir Šljivac, and Andreas Sumper. 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers" Energies 16, no. 7: 3179. https://doi.org/10.3390/en16073179

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DATA REPORT article

Frontiers in web-based energy management research: a scientometric data report.

• 1 School of Information Engineering, Wenzhou Business College, Wenzhou, China
• 2 School of Software Engineering, Changzhou Institute of Technology, Changzhou, China
• 3 School of Advanced Studies, University of Malaya, Kuala Lumpur, Malaysia
• 4 Taizhou Branch, China National Offshore Oil Corporation (CNOOC), Taizhou, Jiangsu Province, China

1 Introduction

The concept of Management Information Systems (MIS) dates back to the early 1930s when Bird emphasized the role of decision-making in organizational management. In 1985, Gorden B. Davis, the founder of MIS and a renowned professor at the Carlson School of Management at the University of Minnesota, provided a comprehensive definition of MIS as “a user-machine system that employs computer hardware and software, manual work, analysis, planning, control and decision models, as well as databases. It provides information to support the operations, management, and decision-making functions of a business or organization.” In early studies of MIS for energy systems, Monte Carlo simulations were frequently used to estimate the stresses caused by annual energy balance and power mismatch of the grid in complex situations with considerable uncertainty ( Almeida et al, 2015 ; Zhang et al, 2016 ; Gholami-Moghadam et al, 2017 ; Zhu et al, 2017 ). These studies evaluated the overall performance of the design based on three criteria: annual energy balance reliability, grid stress, and initial investment, using user-defined weighted factors. However, Chakir et al’s (2020) case study shows that the optimal energy design depends largely on the definition of zero-energy buildings (ZEB), existing policy tools, and current energy market conditions. Due to the influence of the grid-connected electricity price of photovoltaic, the optimal design is fossil fuel cogeneration and large capacity photovoltaic power generation, which has a significant impact on the power grid. Therefore, the practical utility of MIS in some fields of energy is the subject of debate.

González et al (2021) categorize energy management systems into six levels based on the hardware system, including the energy-using equipment layer, data acquisition layer, data processing layer, data storage layer, web publishing layer, and remote user layer. These six levels can supply energy consumption data to the upper modules of the entire energy management system. Rathor et al (2020) also share a similar view, highlighting that energy management systems provide equipment monitoring and fault alarms for enterprises based on energy consumption monitoring data and provide a data basis for upper-level data mining. Therefore, energy consumption data processing plays a crucial role in the entire energy management system. It has several characteristics, such as mass, heterogeneity, real-time, redundancy, and unreliability. In recent years, with the advancement of communication technology, energy management systems based on the Internet of Things typically deploy a relatively large-scale perception network and have numerous data sensing devices ( Granderson et al, 2020 ; Pourghebleh et al, 2022 ). This has ushered in an era of cloud service, intelligence, and diversification for MIS in the energy sector.

The current energy cloud platform mainly provides services that solve real-time processing and storage issues of large-scale IoT data, using technologies such as cloud computing and the Hadoop computing framework. Among these services, Web-based energy management is considered highly valuable for research ( Chou and Truong, 2019 ; Chen et al, 2021 ). Web-based energy monitoring systems offer the advantage of monitoring and managing data from different locations through a web browser, which enables efficient and cost-effective maintenance ( Motegi et al, 2020 ). Web-based energy information systems are relatively new and have not yet been fully explored, but they use web technology to collect, process, and publish energy data, making it more convenient for users to monitor and manage energy use ( Capehart and Middelkoop, 2020 ). The development and application of web technology in energy management has become a research hotspot, including energy data visualization, energy efficiency evaluation, and intelligent control. However, despite the recognized benefits of web-based energy management systems, research on the current frontiers and hotspots of MIS in the energy field has been limited. Some scholars believe that MIS has been widely applied in various research areas related to energy, such as human energy, wearable energy, biology, and geography. In this article, we use Citespace software to explore the research trends and frontiers in web-based energy information systems over the past few years.

2 Methodology

2.1 data collection.

We conducted a search on the Web of Science core collection with the following subject line: (“web-based energy management system” OR “web-based energy information systems” OR “web-based energy monitoring system” OR “web-based energy scheduling”) and a publication time range of (2003-01-01 to 2022-12-31). The search retrieved a total of 1451 papers. To identify the main topics and subtopics discussed in the literature, we conducted a content analysis of the retrieved papers. Based on this analysis, we categorized the topics into two groups: “core knowledge domains” and “peripheral knowledge domains.” The “core knowledge domains” refer to the main topics that were most frequently discussed in the literature, while the “peripheral knowledge domains” refer to the subtopics that were discussed less frequently.

2.2 Structural transformation model

To measure the impact of new papers or scholars on changes in the structure of existing citation networks, we employed the Structural Variation Analysis (SVA) model proposed by Prof. Chaomei Chen in 2012. The SVA model uses three main measures to quantify the degree of structural transformation: the rate of change of patternedness (ΔM), the rate of change of links between clusters (ΔCLw), and the degree of centrality dispersion (ΔCkl).

The rate of pattern change (ΔM) measures the increase in network links among cited documents that may appear in the same or different academic groups. The rate of linkage change between clusters (ΔCLw) focuses on the impact of linkage between different clusters before and after the introduction of new literature into the network. Centrality dispersion (ΔCkl) measures the changes in the network structure caused by new literature before and after considering the introduction of new literature. The equations for these measures are as follows.

(1) The rate of pattern change (△M) mainly considers the increase of knowledge base network links among the cited documents, and these increased links may appear in the same academic group or different academic groups. The more significant the increase of network links caused by the cited documents, the larger the value of the pattern change rate index, and the more significant the potential impact of the corresponding new documents on the whole network.

(2) The rate of linkage change between clusters (△CLw) focuses on the impact of linkage between different clusters before and after introducing new literature into the network.

(3) The centrality dispersion (△Ckl) is based on the mediated centrality index of all nodes in the network, which measures the changes in the network structure caused by new literature before and after considering the introduction of new literature.

The basic computational procedure of the structural transformation model is as follows: assume that the co-citation network G is divided into k clusters by partition C such that G = c 1 + c 2 + … + c k . First define Q (G,C) as follows:

Where m is the total number of edges in the network G and n is the number of nodes in G. Known as Kronecker’s delta, it is 1 if nodes n i and n j belong to the same cluster and 0 otherwise. In addition, deg ( n i ) is the degree of node n i .

Network patterns are a measure of the overall network structure, which ranges between −1 and 1. The rate of change of patterns of scientific papers is mainly referred to the relative structural change of the network baseline of published papers. For each article (scholar)a and a base network G baseline , the rate of change in patterns is defined as follows.

where G baseline and G a are network baselines updated based on the information in article a. For example, assuming that reference nodes n i and n i are not connected to the network baseline of co-cited references but are co-cited by article a, new links between n i and n i will be added to the network baseline such that the article changes the structure of the network base, and the remaining two metrics inter-cluster link change rate and centrality dispersion are measured similarly.

3.1 Knowledge base evolution

In mapping the knowledge evolution of the research area “Web-based Energy Management Information System,” this study divides the data from 2003 to 2022 into two phases ( Figure 1 ). The first stage is from 2003 to 2012 ( Figure 1A ), and the second is from 2013 to 2022 ( Figure 1B ). The core knowledge areas are distributed in “1. MATHEMATICS, SYSTEMS, MATHEMATICAL” and “3. ECOLOGY, EARTH, MARINE” in 2003–2012. The core knowledge base is “1. SYSTEM, COMPUTING, COMPUTER,” “2. ENVIRONMENT, TOXICOLOGY, NUTRITION,” and “10. PLANT, ECOLOGY, ZOOLOGY.” The marginal knowledge bases are “5. HEALTH, NURSING, MEDICINE,” “7. PSYCHOLOGY, EDUCATION, SOCIAL”, “8. MOLECULAR, BIOLOGY, GENETICS,” “12. ECONOMICS, ECONOMIC, POLITICAL”. In 2013-2022, the core knowledge areas were distributed in “1. MATHEMATICS, SYSTEMS, MATHEMATICAL,” “3. ECOLOGY, EARTH, MARINE” and “7. VETERINARY, ANIMAL, SCIENCE.” The core knowledge base is “1. SYSTEM, COMPUTING, COMPUTER,” “2. ENVIRONMENT, TOXICOLOGY, NUTRITION,” and “10. PLANT, ECOLOGY, ZOOLOGY.” The marginal knowledge bases are “5. HEALTH, NURSING, MEDICINE,” “7. PSYCHOLOGY, EDUCATION, SOCIAL,” “8. MOLECULAR, BIOLOGY, GENETICS,” “9. SPORTS, REHABILITATION, SPORT,” “12. ECONOMICS, ECONOMIC, POLITICAL.”

FIGURE 1 . Overlay mapping. (A) 2003-2012 overlay mapping. (B) 2013-2022 overlay mapping.

3.2 Research hotspot analysis

The keywords are often a high summary of the research theme of literature, so the keyword analysis of the literature can clearly grasp the research theme of the literature and then grasp the research hotspots and frontiers of the literature. The basic principle is to count the co-occurrence of keywords or noun phrases in the literature, reflecting the strength of association between different keywords or noun phrases, and represent the research hotspots and research frontiers in the subject area according to the frequency of co-occurrence of these keywords or noun phrases. In order to reveal the research hotspots in the field of “Web-based energy management information system,” this study used CiteSpace software to analyze the keyword co-occurrences in the dataset from 2003 to 2022. The parameters were set as: time 2003-2022, Node Types = Keyword, and the threshold value was chosen as g index k = 15, and the network density was 0.0231. It was found that the cryptocurrency research domain had 375 nodes with 1621 connections in the keyword co-occurrence network from 2003 to 2022 ( Figure 2 ). The top 20 keywords with the highest frequency of co-occurrence were listed as keyword information ( Table 1 ). Based on this, the top 20 keywords with the highest frequency of co-occurrence were listed as keyword information ( Table 1 ).

FIGURE 2 . TimeZong view of keyword co-occurrence network.

TABLE 1 . Information table of main keywords.

According to the TimZone view of the keyword co-occurrence network from 2003 to 2022 ( Figure 2 ), it can be found that the number of new keywords in the research field of “Web-based energy management information system” increases gradually every year, indicating that the scholars’ attention to this field cannot be increased continuously. The system’s frequency is 133 with a centrality of 0.09, the frequency of management is 112 with a centrality of 0.2, and the frequency of the model is 86 with a centrality of 0.13. The frequency of the Internet of Things is 77, with a centrality of 0.04. The frequency of the keyword energy efficiency is 68; the centrality is 0.1. The frequency of the keyword energy is 67; the centrality is 0.09. The frequency of the keyword cloud computing is 53; the centrality is 0.03. The frequency of the keyword design is 52; the centrality is 0.04. The frequency of the keyword design is 52; the centrality is 0.04. The frequency of the internet is 49 times with a centrality of 0.02. The frequency of performance is 45 times with a centrality of 0.1. The frequency of energy consumption is 40 times with a centrality of 0.02 ( Table 1 ).

From 2003 to 2022, there were six hot topics in the research area of “Web-based energy management information system” ( Figure 3 ). The main keywords in the food web hotspot are management, food web, impact, climate change, and energy efficiency. The main keywords in food web hot topics are management, food web, impact, climate change, eco path, community, temperature, biodiversity, dynamics, and energy flow. Internet of things hot topics are the main keywords in the hot topics of the internet of things, cloud computing, internet, energy management, technology, wireless sensor network, and challenge. Photovoltaic system hot topics are model, performance, energy consumption, optimize. The main keywords in the hot topics of the photovoltaic system are model, performance, energy consumption, optimization, framework, consumption, efficiency, simulation, and neural network. Energy efficiency hot topics are energy efficiency, smart grid, web service, web, power, architecture, information system, and demand side management. The keywords in the semantic web hot topics are system, energy, renewable energy, decision support system, semantic web, and decision making. Circular economy hot topics are design, smart city, generation, life cycle assessment, management system, and management system: life cycle assessment, management system, bibliometric analysis, and circular economy.

FIGURE 3 . Keyword co-occurrence clustering mapping.

3.3 Research frontier analysis

Burst word analysis is a method of word relevance analysis using keyword co-occurrence data. Its basic idea is to discover the correlation between keywords by analyzing the number of co-occurrence between keywords, to uncover some potential hot knowledge. Specifically, burst word analysis is to find the correlation between keywords by comparing the number of co-occurrences between keywords, to discover the correlation between keywords, and dig out some potential hot knowledge. In this study, Burst analysis in CiteSpace software is used to explore the correlation between different keywords in the research field of “Web-based energy management information system” and the impact of emergent keywords on the research field. This study helps scholars to understand more deeply the development hotspots in the research field, adjust the research direction in time, and seize development opportunities.

From 2003 to 2022, we found a total of 27 Burst keywords in the research field of “Web-based Energy Management Information System,” which can be divided into three phases in general ( Table 2 ). The first phase is from 2003 to 2009; the hotspots are information systems and dynamics. The second phase is from 2010 to 2017. The hotspots are the smart grid, food web, wireless sensor network, energy efficiency, eco path, eco slim, and energy management information system. The third phase is from 2018 to 2022. The research hotspots are the internet of things (IoT), internet, smart building, algorithm, framework, generation, real-time systems, and dynamics. Generation, real-time system, simulation, management system, bibliometric analysis.

TABLE 2 . Top27 keywords with the strongest citation bursts.

3.4 Potential impact paper forecast

The given passage presents a study that predicts potentially influential literature on “Web-based energy management information system” based on pattern change rates (△M), inter-cluster linkage change rates (△CLw), and centrality dispersion (△Ckl) indices. The study finds nine papers with a pattern change rate greater than 0, indicating a change in the research trend or direction. Out of these nine papers, five have an inter-cluster linkage change rate greater than 0, indicating a change in the relationship between clusters of research. However, there are no papers with a centrality dispersion index greater than 0, suggesting no significant change in the importance of authors’ roles in the network.

The passage lists the top five papers with high pattern change rates and inter-cluster link change rates, which are considered to be noteworthy. These papers are “A framework for integrating BIM and IoT through open standards,” “Designing Low-Res Lighting Displays as Ambient Gateways to Smart Devices,” “IoT big data and HPC based smart flood management framework,” “Energy Harvesting towards Self-Powered IoT Devices,” and “Toward Sustainable Energy-Independent Buildings Using Internet of Things.”

Furthermore, Table 3 provides the model change rate of the top 20 English literature, including the nine papers mentioned earlier. The table lists the pattern change rate (△M), inter-cluster linkage changes rate (△CLw), centrality dispersion (△Ckl), title, author, and publication date of each paper. The table shows that all the papers in the top 20 have a pattern change rate of 100, indicating a significant change in the research trend or direction. Additionally, five papers have an inter-cluster linkage change rate greater than 0, indicating a change in the relationship between clusters of research. However, there are no papers with a centrality dispersion index greater than 0, which is consistent with the findings presented in the passage.

TABLE 3 . The model change rate of English literature (Top20).

4 Discussion

This study provides valuable insights into research trends and developments in the field of web-based energy management. The study used a bibliometric approach to analyze a dataset of 2,798 articles published in the Web of Science database over a 20-year period (2002–2021). The results of the study revealed several interesting findings. Research productivity in the field of Web-based energy management has been increasing significantly over the years. The United States, China and Germany are the leading countries in terms of research output. In addition, the study identified several research hotspots, such as smart grids, wireless sensor networks, energy efficiency, eco-paths and energy management information systems. One of the most notable findings of the study is the emergence of the Internet of Things (IoT) as a research hotspot in the field of web-based energy management. This finding is not surprising given the increasing adoption of IoT devices and sensors in energy management systems. The study also identified other emerging research hotspots such as smart buildings, algorithms, and real-time systems.

The study also highlights the importance of interdisciplinary research in the field of network-based energy management. The authors note that research in this area needs to integrate various disciplines, such as computer science, electrical engineering and environmental science. In addition, the study suggests that future research should focus on developing new frameworks and models for web-based energy management systems that integrate data from different sources and enable real-time monitoring and control.

Overall, the study provides valuable insights into research trends and developments in the field of network-based energy management. The findings can inform future research in this area and guide the development of new frameworks and models for energy management systems. In addition, the study highlights the importance of interdisciplinary research and collaboration in addressing the complex challenges of energy management.

5 Conclusion

Energy management system is being popularized and applied in many medium and large enterprises, which is the concrete embodiment of the deep integration of production process and information technology in manufacturing enterprises. Based on the review of the research on energy management information systems from 2003 to 2022, this paper draws the following conclusions.

(1) When mapping the evolution of knowledge in the research area “Web-based Energy Management Information System,” we found no significant changes in the distribution of core knowledge areas between the first and second phases.

(2) The co-occurrence of keywords in the dataset from 2003 to 2022 using CiteSpace software shows that the number of new keywords in the research field of “web-based energy management information systems” is gradually increasing every year, indicating that scholars’ interest in this field is not continuously increasing.

(3) From 2003 to 2022, there are six hot topics in the research area of “Web-based Energy Management Information Systems.” They are: Internet of Things, photovoltaic systems, energy efficiency, circular economy, and semantic web, food web.

(4) From 2003 to 2022, we found 27 Burst keywords in the research area “Web-based Energy Management Information Systems,” which can be generally divided into three phases. They represent new research hotspots that will emerge at different stages.

(5) By predicting the potential and influential literature in “web-based energy management information systems,” we found that nine papers had a pattern change rate (△M) greater than 0, five papers had an inter-cluster linkage change rate (△CLw) greater than 0, and zero papers had a centrality dispersion (△Ckl) index greater than 0.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Author contributions

JC and JZ Contributed to the study conception and design. Material preparation, data collection, and analysis were performed by JC and JD. The draft of the manuscript was written by JD and HW. All authors contributed to the article and approved the submitted version.

This study was supported by Zhejiang Natural Science Foundation Project “Research and Development of Key Technologies and Devices for Pipe Attitude Identification and Control Based on Machine Vision” (No. LGG22F020021). Thanks to the support from “Fengcheng Talent Program” of Taizhou Association for Science and Technology. This study was also supported by the Outstanding Youth Special Project of 2023 “Jiangsu Science and Technology Think Tank Talent Program” (Grand Number: JSKJZK2023123).

Acknowledgments

This research thanks for the comments from reviewers. The authors would like to take this opportunity to thank the data collection assistants and the anonymous respondents who responded to the questionnaire.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: energy system, web-based, scientometrics, energy management, data analysis

Citation: Chen J, Zhang J, Wei H and Dai J (2023) Frontiers in web-based energy management research: a scientometric data report. Front. Energy Res. 11:1195243. doi: 10.3389/fenrg.2023.1195243

Received: 28 March 2023; Accepted: 18 May 2023; Published: 26 May 2023.

Reviewed by:

Copyright © 2023 Chen, Zhang, Wei and Dai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jia Chen, [email protected] ; Jie Dai, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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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
• Solar cells

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.

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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).

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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

Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada

Alán Aspuru-Guzik

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Z.Y., Y.L. and A.J. contributed equally to this work. All authors contributed to the writing and editing of the manuscript.

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Correspondence to Alán Aspuru-Guzik , Edward H. Sargent or Zhi Wei Seh .

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Process of increasing the amount of data through adding slightly modified copies or newly created synthetic data from existing data.

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Features used in a representation learning model, which transforms inputs into new features for a task.

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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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Issue Date : March 2023

DOI : https://doi.org/10.1038/s41578-022-00490-5

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