Medium-Range Structural Order in Amorphous Arsenic

Medium-range order (MRO) is a key structural feature of amorphous materials, but its origin and nature remain elusive. Here, we reveal the MRO in amorphous arsenic (<i>a</i>-As) using advanced atomistic simulations, based on machine-learned potentials derived using automated workflows. Our simulations accurately reproduce the experimental structure factor of <i>a</i>-As, especially the first sharp diffraction peak (FSDP), which is a signature of MRO. We compare and contrast the structure of <i>a</i>-As with that of its lighter homologue, red amorphous phosphorus (<i>a</i>-P): we find that <i>a</i>-As has a more uniform dihedral-angle distribution, and so we confirm that its structure can be thought of as a 3-fold coordinated continuous random network in first approximation, in contrast to the more molecular-cluster-like structure of <i>a</i>-P. The pressure-dependent structural behaviors of <i>a</i>-As and <i>a</i>-P differ as well, and the origin of the FSDP is closely correlated with the size and spatial distribution of voids in the amorphous networks. Our work provides fundamental insights into MRO in an amorphous elemental system, and more widely it illustrates the usefulness of automation for machine-learning-driven atomistic simulations.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2026-02-26
DOI
https://doi.org/10.1021/jacs.5c18688
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Medium-Range Structural Order in Amorphous Arsenic

Stephen R. Elliott, YuanBin Liu, Volker L. Deringer, J. George et al.
Journal of the American Chemical Society
Machine Learning in Materials Science
article

Medium-Range Structural Order in Amorphous Arsenic

Stephen R. Elliott, YuanBin Liu, Volker L. Deringer, J. George, Y. Zhou, Richard Ademuwagun, Luc Walterbos
article en

Abstract

Medium-range order (MRO) is a key structural feature of amorphous materials, but its origin and nature remain elusive. Here, we reveal the MRO in amorphous arsenic (<i>a</i>-As) using advanced atomistic simulations, based on machine-learned potentials derived using automated workflows. Our simulations accurately reproduce the experimental structure factor of <i>a</i>-As, especially the first sharp diffraction peak (FSDP), which is a signature of MRO. We compare and contrast the structure of <i>a</i>-As with that of its lighter homologue, red amorphous phosphorus (<i>a</i>-P): we find that <i>a</i>-As has a more uniform dihedral-angle distribution, and so we confirm that its structure can be thought of as a 3-fold coordinated continuous random network in first approximation, in contrast to the more molecular-cluster-like structure of <i>a</i>-P. The pressure-dependent structural behaviors of <i>a</i>-As and <i>a</i>-P differ as well, and the origin of the FSDP is closely correlated with the size and spatial distribution of voids in the amorphous networks. Our work provides fundamental insights into MRO in an amorphous elemental system, and more widely it illustrates the usefulness of automation for machine-learning-driven atomistic simulations.

Journal of the American Chemical SocietyVol. 148(9)
Federal Institute For Materials Research and Testing (DE), University of Oxford (GB), Laboratoire de Chimie (FR), Friedrich Schiller University Jena (DE)
Openalex Percentile: Top 43%
Machine Learning in Materials Science
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