Machine learning for a sustainable energy future (2022-10-18T00:00:00.000000Z)

TL;DR

This Perspective proposes Acc(X)eleration Performance Indicators (XPIs) to measure the effectiveness of platforms developed for accelerated energy materials discovery and proposes a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research.

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.

Authors

Zhenpeng Yao

1 papers

Yanwei Lum

1 papers

Andrew K. Johnston

1 papers

TL;DR

Abstract

Authors

References200 items

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