Motif-based sampling of training configurations produces machine learning potentials that accurately predict alloy properties across compositions, as validated against experiments for phase diagrams, melting, short-range order, thermal expansion, and heat capacity.
MLIP-3: Active learning on atomic environments with Moment Tensor Potentials
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abstract
Nowadays, academic research relies not only on sharing with the academic community the scientific results obtained by research groups while studying certain phenomena, but also on sharing computer codes developed within the community. In the field of atomistic modeling these were software packages for classical atomistic modeling, later -- quantum-mechanical modeling, and now with the fast growth of the field of machine-learning potentials, the packages implementing such potentials. In this paper we present the MLIP-3 package for constructing moment tensor potentials and performing their active training. This package builds on the MLIP-2 package (Novikov et al. (2020), The MLIP package: moment tensor potentials with MPI and active learning. Machine Learning: Science and Technology, 2(2), 025002.), however with a number of improvements, including active learning on atomic neighborhoods of a possibly large atomistic simulation.
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Machine learning potentials for modeling alloys across compositions
Motif-based sampling of training configurations produces machine learning potentials that accurately predict alloy properties across compositions, as validated against experiments for phase diagrams, melting, short-range order, thermal expansion, and heat capacity.