Machine learning modeling of the atomic structure and physical properties of alkali and alkaline-earth aluminosilicate glasses and melts

The first version of the machine learning greybox model i-Melt was trained to predict latent and observed properties of K2O-Na2O-Al2O3-SiO2 melts and glasses. Here, we extend the model compositional range, which now allows accurate predictions of properties for glass-forming melts in the CaO-MgO-K2O-Na2O-Al2O3-SiO2 system, including melt viscosity (accuracy equal or better than 0.4 log10 Pa & BULL;s in the 10-1-1015 log10 Pa & BULL;s range), configurational entropy at glass transition (< 1 J mol-1 K-1), liquidus (< 60 K) and glass transition (< 12 K) temperatures, heat capacity (< 3 %) as well as glass density (< 0.02 g cm-3), optical refractive index (< 0.006), Abbe number (< 4), elastic modulus (< 6 GPa), coefficient of thermal expansion (< 1.1 10-6 K-1) and Raman spectra (< 25 %). Uncertainties on predictions also are now provided. The model offers new possibilities to explore how melt/glass properties change with composition and atomic structure.

Automated summary

The researchers extended the composition range of the i-Melt machine learning greybox model to accurately predict properties of glass-forming melts in the CaO-MgO-K2O-Na2O-Al2O3-SiO2 system. These properties include viscosity, entropy, liquidus and glass transition temperatures, heat capacity, density, refractive index, Abbe number, elastic modulus, coefficient of thermal expansion, and Raman spectra.