QM/MM Methods for Crystalline Defects. Part 3: Machine-Learned Interatomic Potentials

2021/06/28 by Huajie Chen, Christoph Ortner, Chen, Huajie +3
Materials Science · #Electron and X-Ray Spectroscopy Techniques #FOS: Mathematics #Machine Learning in Materials Science #Numerical Analysis (math.NA) #X-ray Diffraction in Crystallography

paper · pdf · doi:10.48550/arxiv.2106.14559

openalex publication_date 2021/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We develop and analyze a framework for consistent QM/MM (quantum/classic) hybrid models of crystalline defects, which admits general atomistic interactions including traditional off-the-shell interatomic potentials as well as state of art "machine-learned interatomic potentials". We (i) establish an a priori error estimate for the QM/MM approximations in terms of matching conditions between the MM and QM models, and (ii) demonstrate how to use these matching conditions to construct practical machine learned MM potentials specifically for QM/MM simulations.

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