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We propose an approach to materials prediction that uses a machine-learninginteratomic potential to approximate quantum-mechanical energies and an activelearning algorithm for the automatic selection of an optimal training dataset.Our approach significantly reduces the amount of DFT calculations needed,resorting to DFT only to produce the training data, while structuraloptimization is performed using the interatomic potentials. Our approach is notlimited to one (or a small number of) lattice types (as is the case for clusterexpansion, for example) and can predict structures with lattice types notpresent in the training dataset. We demonstrate the effectiveness of ouralgorithm by predicting the convex hull for the following three systems: Cu-Pd,Co-Nb-V, and Al-Ni-Ti. Our method is three to four orders of magnitude fasterthan conventional high-throughput DFT calculations and explores a wider rangeof materials space. In all three systems, we found unreported stable structurescompared to the AFLOW database. Because our method is much cheaper and exploresmuch more of materials space than high-throughput methods or cluster expansion,and because our interatomic potentials have a systematically improvableaccuracy compared to empirical potentials such as EAM, etc., it will have asignificant impact in the discovery of new alloy phases, particularly thosewith three or more components.

Accelerating high-throughput searches for new alloys with active learning of interatomic potentials