Regensburg 2019 – wissenschaftliches Programm
MM 4.2: Vortrag
Montag, 1. April 2019, 10:30–10:45, H45
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials — •Konstantin Gubaev1, Evgeny Podryabinkin1, Gus Hart2, and Alexander Shapeev1 — 1Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Nobel str. 3, Moscow 143026, Russia — 2Department of Physics and Astronomy, Brigham Young University, Provo, UT 84602, USA
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 density functional theory (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 hulls 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. In all three systems, we found unreported stable structures compared to the AFLOW database.