Issue 29, 2019

Machine learning for renewable energy materials

Abstract

Achieving the 2016 Paris agreement goal of limiting global warming below 2 °C and securing a sustainable energy future require materials innovations in renewable energy technologies. While the window of opportunity is closing, meeting these goals necessitates deploying new research concepts and strategies to accelerate materials discovery by an order of magnitude. Recent advancements in machine learning have provided the science and engineering community with a flexible and rapid prediction framework, showing a tremendous potential impact. Here we summarize the recent progress in machine learning approaches for developing renewable energy materials. We demonstrate applications of machine learning methods for theoretical approaches in key renewable energy technologies including catalysis, batteries, solar cells, and crystal discovery. We also analyze notable applications resulting in significant real discoveries and discuss critical gaps to further accelerate materials discovery.

Graphical abstract: Machine learning for renewable energy materials

Article information

Article type
Review Article
Submitted
04 mar 2019
Accepted
29 apr 2019
First published
30 apr 2019

J. Mater. Chem. A, 2019,7, 17096-17117

Machine learning for renewable energy materials

G. H. Gu, J. Noh, I. Kim and Y. Jung, J. Mater. Chem. A, 2019, 7, 17096 DOI: 10.1039/C9TA02356A

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