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|a 10.1107/S1600576724009282
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|a eng
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|a Cuillier, Paul
|e verfasserin
|4 aut
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|a Integrating machine learning interatomic potentials with hybrid reverse Monte Carlo structure refinements in RMCProfile
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 05.12.2024
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © Paul Cuillier et al. 2024.
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|a 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
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|a Journal Article
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|a interatomic potentials
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|a machine learning
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|a reverse Monte Carlo
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| 650 |
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|a total scattering
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|a Tucker, Matthew G
|e verfasserin
|4 aut
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| 700 |
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|a Zhang, Yuanpeng
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of applied crystallography
|d 1998
|g 57(2024), Pt 6 vom: 01. Dez., Seite 1780-1788
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|g year:2024
|g number:Pt 6
|g day:01
|g month:12
|g pages:1780-1788
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|u http://dx.doi.org/10.1107/S1600576724009282
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