<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.
Authors
- Sean J. Martens (ORCID: https://orcid.org/0000-0002-7982-6408)
- Karen M. Ehrhardt
- Jack D. Sundberg
Institutions
- University of North Carolina at Chapel Hill (US)
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
Funders
- National Science Foundation