Machine learning‐enabled prediction of oxide glasses’ dielectric constants via augmented data and physicochemical descriptors

Abstract Precise tuning of dielectric constants (εr) in oxide glasses is critical for high‐frequency devices in 5G/6G systems, where εr directly governs signal propagation efficiency. A machine learning framework combining data augmentation and physicochemical descriptor integration is developed to address data scarcity. Validated pseudo‐labels are generated via ensemble learning, expanding the dataset from 1503 to 11,029 compositions without distributional shift. The XGBoost model trained on the augmented dataset achieved superior accuracy, with an R2 of 0.96 and an MSE of 0.14. For prediction tasks on unseen data, it reduced the error rate by 48% compared to the non‐augmented model and improved generalization performance by 43% over GlassNet. B2O3 and SiO2 are identified as εr suppressors and BaO and TiO2 as enhancers through SHAP analysis, aligning with network former/modifier roles. Cation‐specific polarizabilities are derived via Clausius–Mossotti regression (R2 = 0.909). Integration of physicochemical descriptors (coordination number and bond strength) enables transferable predictions for Y2O3 and La2O3 containing glasses, with mean deviation 2.46%–4.76%. Crucially, structural descriptors dominate polarizability with 69.9% feature importance, establishing network engineering as the optimal design paradigm. A data‐driven pathway for rational dielectric glass development is thus established.