  Glasses offer a broad range of tunable thermophysical properties that are
linked to their compositions. However, it is challenging to establish a
universal composition-property relation of glasses due to their enormous
composition and chemical space. Here, we address this problem and develop a
metamodel of composition-atomistic structure relation of a class of glassy
material via a machine learning (ML) approach. Within this ML framework, an
unsupervised deep learning technique, viz. convolutional neural network (CNN)
autoencoder, and a regression algorithm, viz. random forest (RF), are
integrated into a fully automated pipeline to predict the spatial distribution
of atoms in a glass. The RF regression model predicts the pair correlation
function of a glass in a latent space. Subsequently, the decoder of the CNN
converts the latent space representation to the actual pair correlation
function of the given glass. The atomistic structures of silicate (SiO2) and
sodium borosilicate (NBS) based glasses with varying compositions and dopants
are collected from molecular dynamics (MD) simulations to establish and
validate this ML pipeline. The model is found to predict the atom pair
correlation function for many unknown glasses very accurately. This method is
very generic and can accelerate the design, discovery, and fundamental
understanding of composition-atomistic structure relations of glasses and other
materials.
