Structure and properties of alkali aluminosilicate glasses and melts: Insights from deep learning

Aluminosilicate glasses and melts are of paramount importance for geo-and materials sciences. They include most mag-mas, and are used to produce a wide variety of everyday materials, from windows to smartphone displays. Despite this impor-tance, no general model exists with which to predict the atomic structure, thermodynamic and viscous properties of aluminosilicate melts. To address this, we introduce a deep learning framework, 'i-Melt', which combines a deep artificial neu-ral network with thermodynamic equations. It is trained to predict 18 different latent and observed properties of melts and glasses in the K2O-Na2O-Al2O3-SiO2 system, including configurational entropy, viscosity, optical refractive index, density, and Raman signals. Viscosity can be predicted in the 10(0)-10(15) log(10) Pa.s range using five different theoretical frameworks (Adam-Gibbs, Free Volume, MYEGA, VFT, Avramov-Milchev), with a precision equal to, or better than, 0.4 log(10) Pa.s on unseen data. Density and optical refractive index (through the Sellmeier equation) can be predicted with errors equal or lower than 0.02 and 0.006, respectively. Raman spectra for K2O-Na2O-Al2O3-SiO2 glasses are also predicted, with a rel-atively high mean error of similar to 25% due to the limited data set available for training. Latent variables can also be predicted with good precisions. For example, the glass transition temperature, T-g, can be predicted to within 19 K, while the melt configu-rational entropy at the glass transition, S-conf(T-g), can be predicted to within 0.8 J mol(-1) K-1. Applied to rhyolite compositions, i-Melt shows that the rheological threshold separating explosive and effusive eruptions correlates with an increase in the fraction of non-bridging oxygens in rhyolite melts as their alkali/Al ratio becomes larger than 1. Exploring further the effect of the K/(K + Na) ratio on the properties of alkali aluminosilicate melts with compositions varying along a simplified alkali magmatic series trend, we observe that K-rich melts have systematically different structures and higher viscosities compared to Na-rich melts. Combined with the effects of the K/(K + Na) ratio on other parameters, such as the solubility, solution mechanisms and speciation of volatile elements, this could ultimately influence the eruptive dynamics of volcanic systems emitting Na-rich or K-rich alkali magmas. (C) 2021 The Authors. Published by Elsevier Ltd.

Automated summary

Aluminosilicate glasses and melts play a crucial role in geo-and materials sciences but a general model for predicting their properties is lacking. A deep learning framework 'i-Melt' is introduced to accurately predict various properties of glasses and melts, including viscosity, density, and optical refractive index. The study shows that high potassium aluminosilicate melts have different structures and higher viscosities, potentially influencing the eruptive dynamics of volcanic systems.