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.

References in corpus (23)

• The Raman Fingerprint of Graphene
• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• Assortative mixing in networks
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• From Point Defects in Graphene to Two-Dimensional Amorphous Carbon
• Machine-learning based interatomic potential for amorphous carbon
• Interatomic Potential for Silicon Defects and Disordered Phases
• Synthesis and properties of free-standing monolayer amorphous carbon
• Environment Dependent Interatomic Potential for Bulk Silicon
• Data-driven learning of total and local energies in elemental boron
• Growth Mechanism and Origin of High $s{p}^3$ Content in Tetrahedral Amorphous Carbon
• Learning the electronic density of states in condensed matter
• Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon
• Evolution of amorphous carbon across densities: an inferential study
• Defect-Dependent Corrugation in Graphene
• Ab initio simulation of amorphous graphite
• Quantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks
• Pentagonal puckering in a sheet of amorphous graphene
• A finite-temperature Monte Carlo algorithm for network forming materials
• Structural dynamics of polycrystalline graphene
• Efficient structural relaxation of polycrystalline graphene models

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