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Interpretable machine learning-assisted screening of perovskite oxides
Jie Zhao1, Xiaoyan Wang2, Haobo Li3
1College of Chemical Engineering, Nanjing Tech University Nanjing Jiangsu 211816 China j.zhao1@njtech.edu.cn.
RSC Advances
|January 29, 2024
Summary
Machine learning models efficiently identify stable perovskite oxides for energy applications. This approach accelerates the discovery of new materials by predicting thermodynamic stability and energy above the convex hull.
Area of Science:
- Materials Science
- Computational Chemistry
- Machine Learning
Background:
- Perovskite oxides are crucial for energy storage and conversion technologies.
- Traditional screening methods (experimental and DFT) are time-consuming and costly.
Purpose of the Study:
- To develop interpretable machine learning models for predicting perovskite oxide stability.
- To accelerate the identification of stable perovskite oxides from vast virtual combinatorial libraries.
Main Methods:
- Constructed classification and regression models to predict thermodynamic stability and energy above the convex hull (Eh).
- Employed SHapley Additive exPlanations (SHAP) for model interpretability.
- Utilized features such as highest occupied molecular orbital energy, elastic modulus, and ionic radius.
Main Results:
- A classification model achieved high accuracy (0.919), precision (0.937), F1-score (0.932), and recall (0.935).
- Successfully screened over 682,000 stable perovskite oxides from more than 1.1 million virtual combinations.
- A regression model predicted Eh values with a coefficient of determination of 0.916, showing good agreement with DFT calculations.
Conclusions:
- Interpretable machine learning offers an efficient alternative to conventional methods for perovskite oxide discovery.
- Key material descriptors like highest occupied molecular orbital energy and elastic modulus are vital for stability prediction.
- The developed models significantly expedite the search for novel perovskite materials for energy applications.
