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Interpretable machine learning model of effective mass in perovskite oxides with cross-scale features

Changjiao Li Zhengtao Huang Hua Hao Zhonghui Shen Guanghui Zhao Ben Xu Hanxing Liu

Journal of Materiomics2025，Vol.11Issue(1)：P.76-85,10.

Journal of Materiomics2025，Vol.11Issue(1)：P.76-85,10.DOI:10.1016/j.jmat.2024.02.008

Interpretable machine learning model of effective mass in perovskite oxides with cross-scale features

Changjiao Li ¹Zhengtao Huang ¹Hua Hao ¹Zhonghui Shen ¹Guanghui Zhao ¹Ben Xu ²Hanxing Liu¹

作者信息

1. State Key Laboratory of Advanced Technology for Materials Synthesis and Processing,Center for Smart Materials and Device Integration,School of Material Science and Engineering,Wuhan University of Technology,Wuhan,430070,China
2. Graduate School of China Academy of Engineering Physics,Beijing,100193,China
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摘要

关键词

Machine learning/Perovskite oxides/Interpretability/Effective mass/Crystal structure

分类

数理科学

引用本文复制引用

Changjiao Li,Zhengtao Huang,Hua Hao,Zhonghui Shen,Guanghui Zhao,Ben Xu,Hanxing Liu..Interpretable machine learning model of effective mass in perovskite oxides with cross-scale features[J].Journal of Materiomics,2025,11(1):P.76-85,10.

基金项目

supported by the National Key Research and Development Program of China(No.2023YFB3812200) （No.2023YFB3812200）

Major Program of the Natural Science Foundation of China(51790490) （51790490）

NSFC-Guangdong Joint Funds of the Natural Science Foundation of China(No.U1601209) （No.U1601209）

the National Key Basic Research Program of China(973 Program)(No.2015CB654601) （973 Program）

Technical Innovation Special Program of Hubei Province(2017AHB055). （2017AHB055）

Journal of Materiomics

ISSN：2352-8478

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