Exploring the configurational space of amorphous graphene with machine-learned atomic energies
Zakariya El-Machachi 1 , Mark Wilson 2 , Volker L. Deringer 1 1 Inorganic Chemistry Laboratory, University of Oxford, UK 2 Physical and Theoretical Chemistry Laboratory, University of Oxford, UK
While many crystalline two–dimensional materials have been realised experimentally over the past decade, few examples exist for their amorphous counterparts. Two-dimensionally extended amorphous carbon (“amorphous graphene”) is a prototype system for disorder in 2D, showcasing a complex and rich configurational space with short and medium-range order, which is yet to be fully understood. Here we report on an atomistic modelling study of amorphous graphene using a machine learning (ML) based force field. ML force fields are typically “trained" on data from highly accurate but computationally costly density functional theory (DFT) computations. Atomistic models created by such ML approaches can achieve near DFT accuracy at a fraction of the computational cost. One key assumption in many of these methods is that the global energy can be separated into sums of local contributions. The extent to which these “machine-learned” local energies are physically meaningful is an interesting research question. 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-neighbour (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.
DD04
© The Author(s), 2023
Made with FlippingBook Learn more on our blog