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

Paul Cuillier; Matthew G Tucker; Yuanpeng Zhang

doi:10.1107/S1600576724009282

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

J Appl Crystallogr. 2024 Oct 29;57(Pt 6):1780-1788. doi: 10.1107/S1600576724009282. eCollection 2024 Dec 1.

Authors

Paul Cuillier¹, Matthew G Tucker², Yuanpeng Zhang²

Affiliations

¹ Department of Materials Science and Engineering The Ohio State University Columbus OH43212 USA.
² Neutron Science Division Oak Ridge National Laboratory Oak Ridge TN37831 USA.

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.

Keywords: interatomic potentials; machine learning; reverse Monte Carlo; total scattering.

Grants and funding

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under grant No. DGE-1343012, as well as the US Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists, Office of Science Graduate Student Research (SCGSR) program. The SCGSR program is administered by the Oak Ridge Institute for Science and Education for the DOE under contract No. DE-SC0014664.