Issue 46, 2022

Exploring the configurational space of amorphous graphene with machine-learned atomic energies

Abstract

Two-dimensionally extended amorphous carbon (“amorphous graphene”) is a prototype system for disorder in 2D, showing a rich and complex configurational space that is yet to be fully understood. Here we explore the nature of amorphous graphene with an atomistic machine-learning (ML) model. We create structural models by introducing defects into ordered graphene through Monte-Carlo bond switching, defining acceptance criteria using the machine-learned local, atomic energies associated with a defect, as well as the nearest-neighbor (NN) environments. We find that physically meaningful structural models arise from ML atomic energies in this way, ranging from continuous random networks to paracrystalline structures. Our results show that ML atomic energies can be used to guide Monte-Carlo structural searches in principle, and that their predictions of local stability can be linked to short- and medium-range order in amorphous graphene. We expect that the former point will be relevant more generally to the study of amorphous materials, and that the latter has wider implications for the interpretation of ML potential models.

Graphical abstract: Exploring the configurational space of amorphous graphene with machine-learned atomic energies

Article information

Article type
Edge Article
Submitted
03 Aug 2022
Accepted
14 Okt 2022
First published
17 Okt 2022
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2022,13, 13720-13731

Exploring the configurational space of amorphous graphene with machine-learned atomic energies

Z. El-Machachi, M. Wilson and V. L. Deringer, Chem. Sci., 2022, 13, 13720 DOI: 10.1039/D2SC04326B

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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