@article{Wang2026, 
author = {Zidong Wang and Chenbo Zhang and Fei Li},
title = {An interpretable machine learning framework for predicting Curie temperature of lead-based piezoelectric ceramics},
year = {2026},
journal = {Journal of Materiomics},
volume = {12},
number = {4},
keywords = {Interpretable machine learning, Lead-based piezoelectric ceramics, Curie temperature prediction},
url = {https://www.sciopen.com/article/10.1016/j.jmat.2026.101247},
doi = {10.1016/j.jmat.2026.101247},
abstract = {Lead-based perovskite piezoelectric ceramics exhibit tunable Curie temperatures through sophisticated compositional design, which is critical for high-temperature applications. However, predicting Tc remains challenging owing to complex compositional spaces. Herein, we develop an interpretable machine learning framework leveraging published experimental data to guide the chemical design of lead-based piezoelectric ceramics. Our methodology involves: (1) exhaustive screening of feature combinations to minimize cross-validation errors, (2) Bayesian optimization of hyperparameters to reduce model error and improve predictive accuracy (R2 > 0.98), and (3) Shapley Additive Explanations and partial dependence analysis to elucidate feature-Tc correlations and mitigate the black-box nature of conventional machine learning. Furthermore, the Sure Independence Screening and Sparsifying Operator method extracts explicit mathematical formulas correlating with experimental Tc values (R2 > 0.90). This work not only advances the rational design of lead-based piezoelectric ceramics for temperature-specific applications but also establishes a paradigm for machine learning in other functional material systems.}
}