Integrating machine learning interatomic potentials with hybrid reverse Monte Carlo structure refinements in RMCProfile

Research output: Contribution to journalArticlepeer-review

Abstract

Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtain solutions that are both physically sensible and in agreement with experiment. To expand the range of materials that can be studied with hybrid RMC, we have implemented a new interatomic potential constraint in RMCProfile that grants flexibility to apply potentials supported by the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular dynamics code. This includes machine learning interatomic potentials, which provide a pathway to applying hybrid RMC to materials without currently available interatomic potentials. To this end, we present a methodology to use RMC to train machine learning interatomic potentials for hybrid RMC applications.

Original languageEnglish
Pages (from-to)1780-1788
Number of pages9
JournalJournal of Applied Crystallography
Volume57
DOIs
StatePublished - Dec 1 2024

Keywords

  • interatomic potentials
  • machine learning
  • reverse Monte Carlo
  • total scattering

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