Modeling viscosity of commercial glass melts: Adam–Gibbs equation and Gaussian process regression

Abstract

A statistically designed property database of six commercial glass families is leveraged to develop models relating viscosity to temperature and composition. Specialized models for each glass family were designed based on the Adam–Gibbs equation. We show that the whole range of viscosity data of one glass family from 100 to 1012 Pa s can be accurately modeled using one composition-dependent and two composition-independent parameters when glass transition temperature and viscosity at that temperature are independently determined. Generalization to a broader composition space using the same approach leads to a loss of accuracy compared to specialized models. However, this can be mostly negated when Gaussian process regression is used to estimate the compositional dependence of configuration entropy and fragility exponent parameters of the Adam–Gibbs equation.

Keywords

• Glass melt
• viscosity
• modeling
• Adam-Gibbs
• Gaussian process regression