A Machine Learning Potential for Graphene

TL;DR

This paper introduces a machine learning interatomic potential for graphene using the GAP methodology, achieving near ab initio accuracy in simulations at a fraction of the computational cost.

Contribution

The paper presents a novel GAP-based interatomic potential for graphene that outperforms traditional empirical potentials in accuracy and efficiency.

Findings

Accurate phonon dispersion curves at 0 K with sub-meV precision

Reliable prediction of finite-temperature phonon spectra and thermal expansion

Excellent agreement with experimental Raman band dispersion data

Abstract

We present an accurate interatomic potential for graphene, constructed using the Gaussian Approximation Potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface, facilitating highly accurate (approaching the accuracy of ab initio methods) molecular dynamics simulations. This is achieved at a computational cost which is orders of magnitude lower than that of comparable calculations which directly invoke electronic structure methods. We evaluate the accuracy of our machine learning model alongside that of a number of popular empirical and bond-order potentials, using both experimental and ab initio data as references. We find that whilst significant discrepancies exist between the empirical interatomic potentials and the reference data - and amongst the empirical potentials themselves - the machine learning model introduced here provides exemplary performance in all of the tested areas. The calculated properties include: graphene phonon dispersion curves at 0 K (which we predict with sub-meV accuracy), phonon spectra at finite temperature, in-plane thermal expansion up to 2500 K as compared to NPT ab initio molecular dynamics simulations and a comparison of the thermally induced dispersion of graphene Raman bands to experimental observations. We have made our potential freely available online at [http://www.libatoms.org].