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In (M Hodapp and A Shapeev 2020 Mach. Learn.: Sci. Technol. 1 045005), wehave proposed an algorithm that fully automatically trains machine-learninginteratomic potentials (MLIPs) during large-scale simulations, and successfullyapplied it to simulate screw dislocation motion in body-centered cubictungsten. The algorithm identifies local subregions of the large-scalesimulation region where the potential extrapolates, and then constructsperiodic configurations of 100--200 atoms out of these non-periodic subregionsthat can be efficiently computed with plane-wave Density Functional Theory(DFT) codes.  In this work, we extend this algorithm to dissociated dislocations witharbitrary character angles and apply it to partial dislocations inface-centered cubic aluminum. Given the excellent agreement with available DFTreference results, we argue that our algorithm has the potential to become auniversal way of simulating dissociated dislocations in face-centered cubic andpossibly also other materials, such as hexagonal closed-packed magnesium, andtheir alloys. Moreover, it can be used to construct reliable training sets forMLIPs to be used in large-scale simulations of curved dislocations.

Automated ab initio-accurate atomistic simulations of dissociated dislocations