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

Journal Article · · Journal of Applied Crystallography (Online)
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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.

Sponsoring Organization:
USDOE
Grant/Contract Number:
SC0014664
OSTI ID:
2475445
Journal Information:
Journal of Applied Crystallography (Online), Journal Name: Journal of Applied Crystallography (Online) Journal Issue: 6 Vol. 57; ISSN 1600-5767; ISSN JACGAR
Publisher:
International Union of Crystallography (IUCr)Copyright Statement
Country of Publication:
Denmark
Language:
English

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