  Viscosity ($\eta$) 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
$\eta(T)$ of oxide liquids. 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, $\eta$ can be computed at any temperature of interest, with
the advantage of good extrapolation capabilities inherent to the MYEGA
equation. The 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 $R^2$ 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.
