DataSheet1_Interpretable machine learning for understanding compositional and testing condition effects on refractive index, density, dielectric constant, and loss tangent of inorganic melts and glasses.PDF

Artificial intelligence (AI) and machine learning (ML) have enabled property-targeted design of glasses. Several machine learning models and open-source tools in the literature allow researchers to predict the optical, physical, mechanical, and electrical properties of glasses as a function of their chemical compositions. However, these properties also depend on testing conditions. In this paper, we train machine learning models by considering composition and wavelength, temperature, and frequency to predict the refractive index, density, and the two electrical properties, i.e., dielectric constant and loss tangent of glasses, respectively. The predictions of trained models are explained using SHAP analysis, revealing that testing conditions, such as wavelength and temperature, interact majorly with network formers while predicting refractive index and density. In the case of electrical properties, network formers and frequency have the highest interactions, followed by network modifiers and intermediates, and hence govern predictions of dielectric constant and loss tangent. Overall, AI/ML models that can predict the properties of glasses as a function of their composition and testing conditions, coupled with SHAP plots, provide a practical tool to develop a range of glasses for application under varying conditions.