Exploring the Core-shell Structure of BaTiO3-based Dielectric Ceramics Using Machine Learning Models and Interpretability Analysis

A machine learning (ML)-based random forest (RF) classification model algorithm was employed to investigate the main factors affecting the formation of the core-shell structure of BaTiO3-based ceramics and their interpretability was analyzed by using Shapley additive explanations (SHAP). An F1-score changed from 0.879 5 to 0.931 0, accuracy from 0.845 0 to 0.907 0, precision from 0.871 4 to 0.900 0, recall from 0.892 9 to 0.964 3, and ROC/AUC value of 0.97 +/- 0.03 was achieved by the RF classification with the optimal set of features containing only 5 features, demonstrating the high accuracy of our model and its high robustness. During the interpretability analysis of the model, it was found that the electronegativity, melting point, and sintering temperature of the dopant contribute highly to the formation of the core-shell structure, and based on these characteristics, specific ranges were delineated and twelve elements were finally obtained that metall the requirements, namely Si, Sc, Mn, Fe, Co, Ni, Pd, Er, Tm, Lu, Pa, and Cm. In the process of exploring the structure of the core-shell, the doping elements can be effectively localized to be selected by choosing the range of features.