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Glasses offer a broad range of tunable thermophysical properties that arelinked to their compositions. However, it is challenging to establish auniversal composition-property relation of glasses due to their enormouscomposition and chemical space. Here, we address this problem and develop ametamodel of composition-atomistic structure relation of a class of glassymaterial via a machine learning (ML) approach. Within this ML framework, anunsupervised deep learning technique, viz. convolutional neural network (CNN)autoencoder, and a regression algorithm, viz. random forest (RF), areintegrated into a fully automated pipeline to predict the spatial distributionof atoms in a glass. The RF regression model predicts the pair correlationfunction of a glass in a latent space. Subsequently, the decoder of the CNNconverts the latent space representation to the actual pair correlationfunction of the given glass. The atomistic structures of silicate (SiO2) andsodium borosilicate (NBS) based glasses with varying compositions and dopantsare collected from molecular dynamics (MD) simulations to establish andvalidate this ML pipeline. The model is found to predict the atom paircorrelation function for many unknown glasses very accurately. This method isvery generic and can accelerate the design, discovery, and fundamentalunderstanding of composition-atomistic structure relations of glasses and othermaterials.

Predicting Pair Correlation Functions of Glasses using Machine Learning