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Cited by 517

The DOI matches its Crossref record; 158 of the 158 references it cites were found in a registry or an index.

Machine learning for a sustainable energy future

Open access
Yao, Zhenpeng, Lum, Yanwei, Johnston, Andrew, Mejia-Mendoza, Luis Martin, Zhou, Xin, Wen, Yonggang, Aspuru-Guzik, Alán, Sargent, Edward H., Seh, Zhi Wei·2022·Nature Reviews Materials
DOI 10.1038/s41578-022-00490-5
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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. Machine learning is poised to accelerate the development of technologies for a renewable energy future. This Perspective highlights recent advances and in particular proposes Acc(X)eleration Performance Indicators (XPIs) to measure the effectiveness of platforms developed for accelerated energy materials discovery.

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Metadata

Authors
Yao, Z., Lum, Y., Johnston, A., Mejia-Mendoza, L. M., Zhou, X., Wen, Y., Aspuru-Guzik, A., Sargent, E. H., & Seh, Z. W.
Venue
Nature Reviews Materials
Publisher
Springer Science and Business Media LLC
Published
2022-10-18
Volume
8
Issue
3
Pages
202-215
ISSN
2058-8437 (ISSN-L)
DOI
10.1038/s41578-022-00490-5
OpenAlex
W4306698380
Cited by
517
References
158
FWCI
4.32
Type
journal article
Language
en
Open access
bronze
Licence

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CoChat. (2026). Citation check: Machine learning for a sustainable energy future. https://app.cochat.ai/papers/10.1038/s41578-022-00490-5
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Yao, Z., Lum, Y., Johnston, A., Mejia-Mendoza, L. M., Zhou, X., Wen, Y., Aspuru-Guzik, A., Sargent, E. H., & Seh, Z. W. (2022). Machine learning for a sustainable energy future. Nature Reviews Materials. https://doi.org/10.1038/s41578-022-00490-5

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