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.

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Cited by in corpus (5)

• Robustness of Local Predictions in Atomistic Machine Learning Models
• Structure and mechanical properties of monolayer amorphous carbon and boron nitride
• Prediction rigidities for data-driven chemistry
• Electronic Conductivity in Metal-Graphene Composites: The Role of Disordered Carbon Structures, Defects, and Impurities
• Strength of 2D Glasses Explored by Machine-Learning Force Fields