Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Machine learning for a sustainable energy future

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

Subjects

Abstract

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.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Traditional and accelerated approaches to materials discovery.
Fig. 2: Examples illustrating the use of ML techniques for a sustainable energy future.
Fig. 3: Areas of opportunity for ML and renewable energy.

Similar content being viewed by others

References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Google Scholar 

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

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  62. Shi, C., Hansen, H. A., Lausche, A. C. & Norskov, J. K. Trends in electrochemical CO2 reduction activity for open and close-packed metal surfaces. Phys. Chem. Chem. Phys. 16, 4720–4727 (2014).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  64. Ulissi, Z. W. et al. Machine-learning methods enable exhaustive searches for active bimetallic facets and reveal active site motifs for CO2 reduction. ACS Catal. 7, 6600–6608 (2017).

    Article  CAS  Google Scholar 

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  71. Huang, Y., Chen, Y., Cheng, T., Wang, L.-W. & Goddard, W. A. Identification of the selective sites for electrochemical reduction of CO to C2+ products on copper nanoparticles by combining reactive force fields, density functional theory, and machine learning. ACS Energy Lett. 3, 2983–2988 (2018).

    Article  CAS  Google Scholar 

  72. Chen, Y., Huang, Y., Cheng, T. & Goddard, W. A. Identifying active sites for CO2 reduction on dealloyed gold surfaces by combining machine learning with multiscale simulations. J. Am. Chem. Soc. 141, 11651–11657 (2019).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  75. Tran, K. & Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat. Catal. 1, 696–703 (2018).

    Article  CAS  Google Scholar 

  76. Zhong, M. et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 581, 178–183 (2020).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

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

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

    Article  Google Scholar 

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

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

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

    Google Scholar 

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

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

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

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

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

    Article  Google Scholar 

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

    Google Scholar 

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

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

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

    Article  Google Scholar 

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

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  140. Sanna, S. et al. Enhancement of the chemical stability in confined δ-Bi2O3. Nat. Mater. 14, 500–504 (2015).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

Download references

Acknowledgements

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

Author information

Author notes

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

Authors and Affiliations

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

    Zhenpeng Yao

  2. 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

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

    Zhenpeng Yao

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

    Zhenpeng Yao

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

    Yanwei Lum & Zhi Wei Seh

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

    Yanwei Lum, Andrew Johnston & Edward H. Sargent

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

    Xin Zhou & Yonggang Wen

  8. Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada

    Alán Aspuru-Guzik

Authors
  1. Zhenpeng Yao
    View author publications

    Search author on:PubMed Google Scholar

  2. Yanwei Lum
    View author publications

    Search author on:PubMed Google Scholar

  3. Andrew Johnston
    View author publications

    Search author on:PubMed Google Scholar

  4. Luis Martin Mejia-Mendoza
    View author publications

    Search author on:PubMed Google Scholar

  5. Xin Zhou
    View author publications

    Search author on:PubMed Google Scholar

  6. Yonggang Wen
    View author publications

    Search author on:PubMed Google Scholar

  7. Alán Aspuru-Guzik
    View author publications

    Search author on:PubMed Google Scholar

  8. Edward H. Sargent
    View author publications

    Search author on:PubMed Google Scholar

  9. Zhi Wei Seh
    View author publications

    Search author on:PubMed Google Scholar

Contributions

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

Corresponding authors

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

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Materials thanks Shijing Sun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

MatD3 database: https://github.com/HybriD3-database/MatD3

Materials Project: https://materialsproject.org/

Glossary

Active learning

Machine learning techniques that can query a user interactively to modify its current strategy (that is, label an input).

Artificial intelligence

(AI). Theory and development of computer systems that exhibit intelligence.

Automatic generation control

A system for adjusting the power output of multiple generators at different power plants, in response to changes in the load.

Closed-loop approach

A technology development pipeline that incorporates automation to go from idea to realization of technology. ‘Closed’ refers to the concept that the system improves with experience and iterations.

Data augmentation

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

Deep belief network

A generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables, with connections between the layers but not between units within each layer.

Deep learning

(DL). Machine learning subfield that is based on neural networks with representation learning.

Generalization

The ability to adapt to new, unseen data, drawn from the same distribution as the one used to create the model.

Generative learning

Machine learning techniques that learn to model the data distribution of a dataset and sample new data points.

Interpretability

Degree to which a human can understand a model’s decision. Interpretability can be used to build trust and credibility.

Inverse design

A design method where new materials and compounds are ‘reverse-engineered’ simply by inputting a set of desired properties and characteristics and then using an optimization algorithm to generate a predicted solution.

Long short-term memory

A special kind of recurrent neural networks that are capable of selectively remembering patterns for a long duration of time.

Machine learning

(ML). Field within artificial intelligence that deals with learning algorithms, which improve automatically through experience (data).

Multi-agent system

A computerized system composed of multiple interacting intelligent agents.

Multi-kernel-ridge regression

The combination of ridge regression (a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated) with multiple kernel techniques.

Multiphysics models

Models that involve the analysis of multiple, simultaneous physical phenomena, which can include heat transfer, fluid flow, deformation, electromagnetics, acoustics and mass transport.

Multiscale modelling

The field of solving problems that have important features at multiple scales of time and/or space.

Neural networks

A neural network is composed of parameterized and optimizable transformations.

Recurrent neural networks

A class of artificial neural networks where connections between nodes form a directed or undirected graph along a temporal sequence.

Regularization

Process of incorporating additional information into the model to constrain its solution space.

Reinforcement learning

Machine learning techniques that make a sequence of decisions to maximize a reward.

Representations

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

Retrosynthesis

Technique for solving problems in the planning of chemical synthesis.

Robotic workflows

A robotic equipment automated chemical synthesis plan.

Screening strategy

Design process composed of several stages where materials are iteratively filtered and ranked to arrive to a few top candidates.

Supervised learning

Machine learning techniques that involve the usage of labelled data.

Transfer learning

Machine learning techniques that adapt a learned representation or strategy from one dataset to another.

Uncertainty quantification

Process of evaluating the statistical confidence of model.

Unsupervised learning

Machine learning techniques that learn patterns from unlabelled data.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue date:

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

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics