TY - JOUR
T1 - Machine learning for a sustainable energy future
AU - Yao, Zhenpeng
AU - Lum, Yanwei
AU - Johnston, Andrew
AU - Mejia-Mendoza, Luis Martin
AU - Zhou, Xin
AU - Wen, Yonggang
AU - Aspuru-Guzik, Alán
AU - Sargent, Edward H.
AU - Seh, Zhi Wei
N1 - Publisher Copyright:
© 2022, Springer Nature Limited.
PY - 2023/3
Y1 - 2023/3
N2 - 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.
AB - 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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U2 - 10.1038/s41578-022-00490-5
DO - 10.1038/s41578-022-00490-5
M3 - Article
C2 - 36277083
AN - SCOPUS:85140127540
SN - 2058-8437
VL - 8
SP - 202
EP - 215
JO - Nature Reviews Materials
JF - Nature Reviews Materials
IS - 3
ER -