MLIP-3: Active learning on atomic environments with Moment Tensor Potentials

2023/04/25 by Evgeny V. Podryabinkin, Kamil Garifullin, Podryabinkin, Evgeny +5 · 10 citations
Materials Science · #Atomic Physics (physics.atom-ph) #Computational Physics (physics.comp-ph) #Electron and X-Ray Spectroscopy Techniques #FOS: Physical sciences #Machine Learning in Materials Science #X-ray Diffraction in Crystallography

paper · pdf · doi:10.48550/arxiv.2304.13144

openalex publication_date 2023/04/25 · openalex created_date 2023/04/28 · openalex updated_date 2026/07/28

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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