TY  - EJOU
AU  - Liang, Yalong 
AU  - Yuan, Xiaohui 
AU  - Han, Yuning 
AU  - Li, Pei 

TI  - Structure-Informed Machine Learning for Multi-Property Prediction of NBT-Based Lead-Free Piezoceramics
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 89
IS  - 2
SN  - 1546-2226

AB  - NBT-based lead-free piezoceramics are promising alternatives to Pb-containing materials, yet their functional properties arise from complex and coupled composition–processing–structure–property relationships. Here, we develop a structure-informed machine learning framework to predict and interpret the piezoelectric coefficient <i>d</i><sub>33</sub>, depolarization temperature <i>T</i><sub>d</sub>, and relative dielectric permittivity <i>ε</i><sub>r</sub>. A database of 214 records from 34 publications was compiled, including 204 records for model development and 10 records for independent literature validation. The correlation analysis involved 38 variables, including 35 candidate input descriptors and three target properties. After Pearson correlation-based redundancy filtering, 30 nonredundant input descriptors were retained for model development. To incorporate physically meaningful structural information, structural feature variables were introduced to describe phase-boundary characteristics, local lattice distortion, and effective phase state, thereby improving the interpretability of composition/processing–property relationships. ExtraTreesRegressor (ETR), deep neural networks (DNNs), and residual network (ResNet)-style multilayer perceptrons (MLPs) were evaluated using random five-fold cross-validation, while Bayesian neural networks (BNNs) were used for uncertainty quantification and SHAP analysis was used to identify influential descriptors. Under random five-fold cross-validation, the best-performing models achieved R<sup>2</sup> values of 0.79, 0.89, and 0.90 for <i>d</i><sub>33</sub>, <i>T</i><sub>d</sub>, and <i>ε</i><sub>r</sub>, respectively, suggesting that structural descriptors provide informative physical features for property prediction. SHAP results further highlighted the important role of processing-related descriptors, particularly calcination parameters, in tuning dielectric responses. This framework provides a physically interpretable and uncertainty-aware strategy for candidate screening and optimization of high-performance NBT-based lead-free piezoceramics.
KW  - Lead-free piezoelectric ceramics; NBT-based ceramics; structure-informed machine learning; composition–processing–structure–property relationship; morphotropic phase boundary; interpretable materials informatics

DO  - 10.32604/cmc.2026.086403