<scp>pyHRMC</scp> : Hybrid Reverse Monte Carlo for Electron Total Scattering

Amorphous materials exhibit certain advantageous properties compared to their crystalline counterparts, yet structural characterization is challenging due to their lack of long-range order. This issue is further compounded with amorphous nanomaterials, where strong probe-sample interactions are required. Although electron scattering measurements can address these challenges, extracting detailed structural insights remains difficult. To address this issue, we introduce pyHRMC, a Python package for Hybrid Reverse Monte Carlo (HRMC) analysis of electron total scattering. pyHRMC iteratively refines atomic positions to match experimental electron scattering, generating experimentally-derived atomistic structures. In contrast to previous HRMC software, which uses x-ray and neutron scattering, pyHRMC uses total electron scattering to facilitate investigations of amorphous nanomaterials. We demonstrate that pyHRMC outperforms conventional Reverse Monte Carlo when modeling Al<sub>2</sub>O<sub>3</sub> by more closely replicating a target structure from the electron total scattering. The pyHRMC package provides researchers with a powerful tool for gaining structural insights into nanoscale amorphous materials.

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Publication Details

Journal
Journal of Computational Chemistry
Published
2026-01-19
DOI
https://doi.org/10.1002/jcc.70306
Primary Topic
Advanced Electron Microscopy Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

pyHRMC : Hybrid Reverse Monte Carlo for Electron Total Scattering

Sean J. Martens, Karen M. Ehrhardt, Jack D. Sundberg
Journal of Computational Chemistry
Advanced Electron Microscopy Techniques and Applications
article

pyHRMC : Hybrid Reverse Monte Carlo for Electron Total Scattering

Sean J. Martens, Karen M. Ehrhardt, Jack D. Sundberg
article en

Abstract

Amorphous materials exhibit certain advantageous properties compared to their crystalline counterparts, yet structural characterization is challenging due to their lack of long-range order. This issue is further compounded with amorphous nanomaterials, where strong probe-sample interactions are required. Although electron scattering measurements can address these challenges, extracting detailed structural insights remains difficult. To address this issue, we introduce pyHRMC, a Python package for Hybrid Reverse Monte Carlo (HRMC) analysis of electron total scattering. pyHRMC iteratively refines atomic positions to match experimental electron scattering, generating experimentally-derived atomistic structures. In contrast to previous HRMC software, which uses x-ray and neutron scattering, pyHRMC uses total electron scattering to facilitate investigations of amorphous nanomaterials. We demonstrate that pyHRMC outperforms conventional Reverse Monte Carlo when modeling Al<sub>2</sub>O<sub>3</sub> by more closely replicating a target structure from the electron total scattering. The pyHRMC package provides researchers with a powerful tool for gaining structural insights into nanoscale amorphous materials.

Journal of Computational ChemistryVol. 47(3)
University of North Carolina at Chapel Hill (US)
National Science Foundation
Openalex Percentile: Top 86%
Advanced Electron Microscopy Techniques and Applications
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