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Machine Learning for Renewable Energy Materials


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

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Publication details

The article was received on 04 Mar 2019, accepted on 29 Apr 2019 and first published on 30 Apr 2019

Article type: Review Article
DOI: 10.1039/C9TA02356A
J. Mater. Chem. A, 2019, Accepted Manuscript

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    Machine Learning for Renewable Energy Materials

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

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