.png){kind=icon}
.png){kind=icon}
Return
Explainable ensemble learning for predicting mechanical properties of ABX3 perovskites using elemental composition descriptors
.png){kind=icon}
PRE
AI
DOI：10.1016/j.rechem.2026.103166.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This study presents a data-driven modelling alternative to conventional Density Functional Theory (DFT) calculations by employing three explainable ensemble learning algorithms (CatBoost, XGBoost, and Random Forest) to predict and interpret mechanical properties (bulk, shear, and Young's moduli) of ABX3 perovskites based on elemental descriptors. All models demonstrated strong predictive performance, achieving R2 values of >= 0.93 in both the training and testing phases. The Random Forest model consistently outperformed other models, yielding the highest R2 scores, along with the lowest mean absolute error (MAE) and root mean squared error (RMSE), indicating superior generalization and robustness across all target properties. To enhance interpretability, Shapley Additive Explanations (SHAP) was used to quantify the contribution of individual features to model predictions. Feature importance analysis revealed that the melting points of the A and B site elements are among the most influential predictors of bulk modulus, this is likely due to their correlation with the material's resistance to thermal vibrations and mechanical deformation. Overall, this work demonstrates that explainable ensemble learning models, particularly Random Forest, can serve as scalable and cost-effective tools for accurately predicting and interpreting mechanical properties in perovskite materials. The integration of model interpretability with predictive insight offers a promising pathway for accelerating materials discovery and design in computational materials science.
Keywords:
Machine learning
Mechanical properties
Perovskites
Journal
IF:
4.2
Papers:
506
Citations:
6.1K
Organization
View more organizations