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A phase-separated borosilicate glass, with a relative permittivity ranging from 3 to 3.5 and a loss tangent as low as 5.6 × 10−4, is presented for packaging applications for the next generation of mobile communications. Ionic polarizability for each borosilicate composition was calculated from the Clausius–Mossotti relationship for both the vitreous and crystalline structures, and the polarizability difference between the two is proportional to the dielectric loss. Small amounts of alkali modifier were added to improve the glass processability, and the loss tangent increased to the 1–7 × 10−3 range. The resulting glass is phase-separated, which has no impact in the millimeter-wave spectrum, as the wavelengths are considerably greater than the length scale of each immiscible phase. 

Abstract Machine learning (ML) is emerging as a powerful tool to predict the properties of materials, including glasses. Informing ML models with knowledge of how glass composition affects short-range atomic structure has the potential to enhance the ability of composition-property models to extrapolate accurately outside of their training sets. Here, we introduce an approach wherein statistical mechanics informs a ML model that can predict the non-linear composition-structure relations in oxide glasses. This combined model offers an improved prediction compared to models relying solely on statistical physics or machine learning individually. Specifically, we show that the combined model accurately both interpolates and extrapolates the structure of Na₂O–SiO₂glasses. Importantly, the model is able to extrapolate predictions outside its training set, which is evidenced by the fact that it is able to predict the structure of a glass series that was kept fully hidden from the model during its training.