Predicting Intermetallic Surface Energies with High-Throughput DFT and Convolutional Neural Networks

Surface energy of inorganic crystals is crucial in understanding experimentally-relevant surface properties and thus important in designing materials for many applications including catalysis. Predictive methods and datasets exist for surface energies of monometallic crystals but predicting these properties for bimetallic or more complicated surfaces is an open challenge. Here we present a workflow for predicting surface energies \textit{ab initio} using high-throughput DFT and a machine learning framework. We calculate the surface energy of 3,285 intermetallic alloys with combinations of 36 elements and 47 space groups. We used this high-throughput workflow to seed a database of surface energies, which we used to train a crystal graph convolutional neural network (CGCNN). The CGCNN model was able to predict surface energies with a mean absolute test error of 0.0082 eV/angstrom^2 and can qualitatively reproduce nanoparticle surface distributions (Wulff constructions). Our workflow provides quantitative insights into which surfaces are more stable and therefore more realistic. It allows us to down-select interesting candidates that we can study with robust theoretical and experimental methods for applications such as catalysts screening and nanomaterials synthesis.