Due to their disordered structure, glasses present a unique challenge in
predicting the composition-property relationships. Recently, several attempts
have been made to predict the glass properties using machine learning
techniques. However, these techniques have the limitations, namely, (i)
predictions are limited to the components that are present in the original
dataset, and (ii) predictions towards the extreme values of the properties,
important regions for new materials discovery, are not very reliable due to the
sparse datapoints in this region. To address these challenges, here we present
a low complexity neural network (LCNN) that provides improved performance in
predicting the properties of oxide glasses. In addition, we combine the LCNN
with physical and chemical descriptors that allow the development of universal
models that can provide predictions for components beyond the training set. By
training on a large dataset (~50000) of glass components, we show the LCNN
outperforms state-of-the-art algorithms such as XGBoost. In addition, we
interpret the LCNN models using Shapely additive explanations to gain insights
into the role played by the descriptors in governing the property. Finally, we
demonstrate the universality of the LCNN models by predicting the properties
for glasses with new components that were not present in the original training
set. Altogether, the present approach provides a promising direction towards
accelerated discovery of novel glass compositions.Comment: 15 pages, 3 figure