Designing high-temperature consolidated BaTiO 3 ceramics requires understanding coupled relationships among processing, microstructure, and functional properties. This study proposes an experimental–computational fusion workflow to predict four properties (dielectric constant, tangent loss, density, and ) from 8191 experimental records enriched with density functional theory (DFT)-informed descriptors. Missing experimental values are imputed using K-nearest neighbors (KNN) with domain-consistency checks to preserve physically plausible ranges. To mitigate cross-domain imbalance and expand structural descriptor coverage, the smaller computational descriptor set is augmented using CTGAN, followed by distributional-fidelity assessment using KL divergence, the Wasserstein distance, the Kolmogorov–Smirnov statistic, the overlap area, and correlation preservation. Feature relevance is screened using Pearson correlation and mutual information with redundancy pruning, and a TabTransformer regressor is trained under leakage-free preprocessing with five-fold cross-validation and an independent 10% hold-out test. Compared with experimental-only and computational-only learning (average of 0.7318 and 0.6684), the fused model achieves stable multi-property prediction with average , MAE=0.0206, and RMSE=0.0801. The results further show that applying feature filtering after fusion preserves interaction-dependent predictors, whereas early filtering can degrade tangent loss prediction. Overall, the proposed fusion framework enables accurate screening of processing outcomes using complementary experimental and physics-informed descriptors. • Integrates experimental and DFT-informed descriptors for BaTiO 3 ceramics. • Demonstrates that post-fusion feature selection improves multi-property prediction stability. • Shows interaction-sensitive targets (e.g., tangent loss) degrade under pre-fusion filtering. • Implements leakage-free 5-fold cross-validation with strict hold-out evaluation. • Establishes a fusion-first modeling strategy for processing–structure–property prediction.
Faseeh et al. (Sun,) studied this question.