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Energy Mater 2021;1:[Accepted].10.20517/energymater.2021.10@The Author(s) 2021
Accepted Manuscript
Open AccessArticle

Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage


Correspondence Address: Prof. Bin Lin, School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China, (E-mail): bin@uestc.edu.cn

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© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Abstract

Perovskite materials are central to the fields of energy conversion and storage, especially for fuel cells, but are hindered by the unavoidable huge complexity, with a strong desire for new materials discovery at high speed and high precision. Herein, we propose a new approach with combination of extreme feature engineering and automated machine learning to adaptively learn structure-composition-property relationships of perovskite oxide materials for energy conversion and storage. Structure-composition-property relationships between stability and other features of perovskites are investigated. Extreme feature engineering is used to construct a great quantity of fresh descriptors and a crucial subset of 23 descriptors is acquired by sequential forward selection algorithm. The best descriptor 

for stability of perovskites is selected out by linear regression. The results demonstrate a high-efficient and non-priori-knowledge investigation of structure-composition-property relationships for perovskite materials, providing a new road to discover advanced energy materials.

Cite This Article

Deng Q, Lin B. Automated machine learning structure-composition-property relationships of perovskite materials for energy conversion and storage. Energy Mater 2021;1:[Accept]. https://dx.doi.org/10.20517/energymater.2021.10

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