Utilizing Convolutional Neural Networks to Predict Properties of Inorganic Compounds

Incorporating structural information in machine learning (ML) models of materials properties has been shown to improve the predictive accuracy of the resulting models. In this study, we demonstrate a novel use of convolution neural networks (CNN) to extract information about the crystal structure of inorganic compounds. The CNN-extracted structural properties are then combined with composition-dependent elemental properties to form the material representation for our ML model. We illustrate this method using datasets consisting of high-throughput density functional theory (DFT) data of formation energies of compounds. We critically examine the accuracy of different representations of the compound crystal structure as input for the CNN. We train ML models on ~200,000 entries taken from the Open Quantum Materials Database and evaluate the predictive accuracy on a test set of 20,000 compounds. We compare the predictive accuracy of our CNN model for formation energies with other recently-proposed structural representations, e.g., those based on Voronoi tessellation.