ViscNet: Neural network for predicting the fragility index and the temperature-dependency of viscosity

Viscosity is one of the most important properties of disordered matter. The temperature-dependence of viscosity is used to adjust process variables for glass-making, from melting to annealing. The aim of this work was to develop a physics-informed machine learning model capable of predicting the temperature-dependence of the viscosity of oxide liquids, inspired by the recent Neural Network (NN) reported by Tandia and co-authors. Instead of predicting the viscosity itself, the NN predicts the parameters of the MYEGA viscosity equation: the liquid's fragility index, the glass transition temperature, and the asymptotic viscosity. With these parameters, viscosity can be computed at any temperature of interest, with the advantage of good extrapolation capabilities inherent to the MYEGA equation. The viscosity dataset was collected from the SciGlass database; only oxide liquids with enough data points in the high and low viscosity regions were selected, resulting in a final dataset with 17,584 data points containing 847 different liquids. About 600 features were engineered from the liquids' chemical composition and 35 of these features were selected using a feature selection protocol. The hyperparameter (HP) tuning of the NN was performed in a set of experiments using both random search and Bayesian strategies, where a total of 700 HP sets were tested. The most successful HP sets were further tested using 10-fold cross-validation, and the one with the lowest average validation loss was selected as the best set. The final trained NN was tested with a test dataset of 85 liquids with different compositions than those used for training and validating the NN. The coefficient of determination (R-2) for the test dataset's prediction was 0.97. This work introduces three advantages: the model can predict viscosity as well as the liquids' glass transition temperature and fragility index; the model is designed and trained with a focus on extrapolation; finally, the model is available as free and open-source software licensed under the GPL3. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Automated summary

The study developed a physics-informed machine learning model capable of predicting the temperature-dependence of the viscosity of oxide liquids with good extrapolation capabilities.