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A machine learning approach for predicting thermal expansion in perovskite oxide cathodes via tolerance factor τ minimization
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DOI：10.1016/j.fuproc.2026.108464.png)
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Abstract
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• ML predicts thermal expansion coefficients (TECs) of perovskite oxides. • Tolerance factor minimization solves the high-entropy issue. • Model yields R2 = 0.81 and MAE = 1.27 × 10−6 K−1. • The SHAP analysis reveals the key features for predicting TECs. • Model has strong generalization to high-entropy perovskites.
Keywords:
Perovskite
Thermal expansion coefficient
Tolerance factor
Machine learning
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