Machine learning for a sustainable energy future

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Machine learning for a sustainable energy future
Title:
Machine learning for a sustainable energy future
Journal Title:
Nature Reviews Materials
Publication Date:
18 October 2022
Citation:
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
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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - NRF Fellowship
Grant Reference no. : NRF-NRFF2017-04

This research / project is supported by the National Research Foundation - Green Buildings Innovation Cluster
Grant Reference no. : NRF2015ENC-GBICRD001-012

This research / project is supported by the National Research Foundation - Green Data Centre Research
Grant Reference no. : NRF2015ENC-GDCR01001-003

This research / project is supported by the National Research Foundation - Energy Programme
Grant Reference no. : NRF2017EWT-EP003-023

Overseas Funding 1) Nanoporous Materials Genome Center by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under award number DE-FG02-17ER16362 2) US Department of Energy, Office of Science - Chicago under Award Number DE-SC0019300. 3) Huawei Technologies Canada Co., Ltd. 4) Natural Sciences and Engineering Research Council (NSERC) 5) Defense Advanced Research Projects Agency under the Accelerated Molecular Discovery Program under Cooperative Agreement No. HR00111920027 dated August 1, 2019. 6) Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow. 7) Ontario Ministry of Colleges and Universities (Grant ORF-RE08-034) 8) Natural Sciences and Engineering Research Council (NSERC) of Canada (Grant RGPIN-2017-06477) 9) Canadian Institute for Advanced Research (CIFAR) (Grant FS20-154 APPT.2378) 10) University of Toronto Connaught Fund (Grant GC 2012-13)
Description:
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1038/s41578-022-00490-5
ISSN:
2058-8437
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