A physics-informed graph-ensemble framework for predicting structure-dependent dielectric properties.

Predicting the structure-dependent dielectric responses of high-k oxides remains a fundamental bottleneck in the development of next-generation nanoelectronics, primarily due to the complex nature of ionic polarization. In this work, we propose a physics-informed hybrid framework designed for the performance prediction of these materials. We explicitly decouple the total dielectric constant into its electronic (εel) and ionic (εion) contributions. To capture the multi-body interactions and bond-angle distortions that govern εion, we employ the Atomistic Line Graph Neural Network (ALIGNN). Crucially, the extracted structure-aware representations are coupled with a pre-training strategy and refined via an XGBoost ensemble regressor within a stacking architecture. This approach achieves high predictive accuracy, yielding an R2 of 0.943 for εel and 0.791 for the inherently challenging εion. Furthermore, it reduces the log-domain mean absolute error (MAE) of the total dielectric constant to 0.073, corresponding to a physical-domain MAE of ≈2.9, demonstrating improvements over both pure tree-based baselines and vanilla graph networks. Deploying this framework for high-throughput screening, we evaluated candidate oxides against stringent criteria, including a wide bandgap threshold Eg > 4.0 eV to suppress leakage currents. The pipeline successfully identified three promising high-k candidates, (Sr3Hf2O7, SrHfO3, Li2HfO3) offering a physically interpretable and scalable route for data-driven performance predictions of advanced electronic materials.