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

TL;DR

This study uses machine learning to explore the structural diversity of amorphous graphene, demonstrating how atomic energies guide the creation of realistic models and reveal local stability and order.

Contribution

It introduces a novel ML-based approach to generate and analyze amorphous graphene structures, linking atomic energies to local stability and structural order.

Findings

ML atomic energies guide Monte-Carlo structural searches

Models range from random networks to paracrystalline structures

ML predictions relate to local stability and medium-range order

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.