# Medium-Range Structural Order in Amorphous Arsenic

**Authors:** Yuanbin Liu, Yuxing Zhou, Richard Ademuwagun, Luc Walterbos, Janine George, Stephen R. Elliott, Volker L. Deringer

PMC · DOI: 10.1021/jacs.5c18688 · 2026-02-26

## TL;DR

This paper uses advanced simulations to uncover the medium-range order in amorphous arsenic and compares it to amorphous phosphorus.

## Contribution

The study reveals the structural nature of medium-range order in amorphous arsenic using machine-learned potentials and automated simulations.

## Key findings

- Amorphous arsenic has a more uniform dihedral-angle distribution compared to amorphous phosphorus.
- The first sharp diffraction peak in amorphous arsenic is linked to the size and distribution of voids in its network.
- Automation enhances the accuracy and efficiency of machine-learning-driven atomistic simulations.

## 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 (a-As) using advanced
atomistic simulations, based on machine-learned potentials derived
using automated workflows. Our simulations accurately reproduce the
experimental structure factor of a-As, especially
the first sharp diffraction peak (FSDP), which is a signature of MRO.
We compare and contrast the structure of a-As with
that of its lighter homologue, red amorphous phosphorus (a-P): we find that a-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 a-P. The pressure-dependent structural
behaviors of a-As and a-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.

## Full-text entities

- **Chemicals:** Arsenic (MESH:D001151), a-As (-)

## Figures

18 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12983317/full.md

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Source: https://tomesphere.com/paper/PMC12983317