  We propose an approach to materials prediction that uses a machine-learning
interatomic potential to approximate quantum-mechanical energies and an active
learning 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 structural
optimization is performed using the interatomic potentials. Our approach is not
limited to one (or a small number of) lattice types (as is the case for cluster
expansion, for example) and can predict structures with lattice types not
present in the training dataset. We demonstrate the effectiveness of our
algorithm 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 faster
than conventional high-throughput DFT calculations and explores a wider range
of materials space. In all three systems, we found unreported stable structures
compared to the AFLOW database. Because our method is much cheaper and explores
much more of materials space than high-throughput methods or cluster expansion,
and because our interatomic potentials have a systematically improvable
accuracy compared to empirical potentials such as EAM, etc., it will have a
significant impact in the discovery of new alloy phases, particularly those
with three or more components.
