Automated atomistic simulations of dissociated dislocations with ab initio accuracy

In a previous work [M. Hodapp and A. Shapeev, Mach. Learn.: Sci. Technol. 1, 045005 (2020)], we proposed an algorithm that fully automatically trains machine-learning interatomic potentials (MLIPs) during large-scale simulations, and successfully applied it to simulate screw dislocation motion in body-centered-cubic tungsten. The algorithm identifies local subregions of the large-scale simulation region where the potential extrapolates, and then constructs periodic configurations of 100–200 atoms out of these nonperiodic subregions that can be efficiently computed with plane-wave density functional theory (DFT) codes. In this work, we extend this algorithm to dissociated dislocations with arbitrary character angles and apply it to partial dislocations in face-centered-cubic aluminum. Given the excellent agreement with available DFT reference results, we argue that our algorithm has the potential to become a universal way of simulating dissociated dislocations in face-centered-cubic and possibly other materials, such as hexagonal-closed-packed magnesium, and their alloys. Moreover, it can be used to construct reliable training sets for MLIPs to be used in large-scale simulations of curved dislocations.