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 struc

SUBMITTER: El-Machachi Z 

PROVIDER: S-EPMC9710228 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature