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, and later for quantum-mechanical modeling; currently, with the fast growth of the field of machine-learning potentials, the packages implement 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., “The MLIP package: moment tensor potentials with MPI and active learning,” Mach. Learn.: Sci. Technol., 2(2), 025002 (2020)], however, with a number of improvements, including active learning on atomic neighborhoods of a possibly large atomistic simulation.
MLIP-3: Active learning on atomic environments with moment tensor potentials
Evgeny Podryabinkin, Kamil Garifullin, Alexander Shapeev, Ivan Novikov; MLIP-3: Active learning on atomic environments with moment tensor potentials. J. Chem. Phys. 28 August 2023; 159 (8): 084112. https://doi.org/10.1063/5.0155887
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