Multiobjective optimization of dielectric, thermal, and mechanical properties of inorganic glasses utilizing explainable machine learning and genetic algorithm

Abstract

To meet the demands of advanced electronic devices, inorganic glasses are required to have comprehensive dielectric, thermal, and mechanical properties. However, the complex composition–property relationship and vast compositional diversity hinder optimization. This study developed machine learning models to predict permittivity, dielectric loss, thermal conductivity, coefficient of thermal expansion, and Young’s modulus based on the composition features of inorganic glasses. The optimal models achieve R² values of 0.9614, 0.7411, 0.9454, 0.9684, and 0.8164, respectively. By integrating domain knowledge with model-agnostic interpretation methods, feature contributions and interactions were analyzed. The mixed alkali effect is crucial for property regulation, especially Na-K for dielectric loss and Na-Li for thermal conductivity. Boron anomaly shifts the high-λ region to a balanced composition of alkali metals with rising B%. The multiobjective optimization of properties was realized using a genetic algorithm framework. After 23 iterations, the optimal material in the MgO-Al₂O₃-B₂O₃-SiO₂ system exhibits ε_r = 4.78, tanδ = 0.00063, λ = 2.59 W/(m·K), α = 50.27×10⁻⁷K⁻¹, and E = 82.41 GPa, outperforming all materials in the dataset. The computational effort was reduced to 1/19 of that required using exhaustive search methods. This study provides a model interpretation framework and an effective multiobjective optimization strategy for glass design.