Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method

Zhai, Ximei; Ding, Fei; Zhao, Zeyu; Santomauro, Aaron; Luo, Feng; Tong, Jianhua

doi:10.1038/s43246-022-00269-9

Title: Predicting the formation of fractionally doped perovskite oxides by a function-confined machine learning method

Journal Article · 05 July 2022 · Communications Materials

DOI: https://doi.org/10.1038/s43246-022-00269-9 · OSTI ID:1875008

Zhai, Ximei; Ding, Fei;

; Santomauro, Aaron;

;

Abstract Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes.

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Research Organization:: Clemson University, SC (United States)

Sponsoring Organization:: National Aeronautics and Space Administration (NASA); US National Science Foundation (NSF); USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Hydrogen Fuel Cell Technologies Office (HFTO)

Grant/Contract Number:: EE0008428

OSTI ID:: 1875008

Journal Information:: Communications Materials, Journal Name: Communications Materials Journal Issue: 1 Vol. 3; ISSN 2662-4443

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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