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Development of a machine learning potential for graphene

Rowe, P; Csanyi, G; Alfe, D; Michaelides, A; (2018) Development of a machine learning potential for graphene. Physical Review B , 97 (5) , Article 054303. 10.1103/PhysRevB.97.054303. Green open access

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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].

Type: Article
Title: Development of a machine learning potential for graphene
Open access status: An open access version is available from UCL Discovery
DOI: 10.1103/PhysRevB.97.054303
Publisher version: http://doi.org/10.1103/PhysRevB.97.054303
Language: English
Additional information: © 2018 American Physical Society. This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Physical Sciences, Physics, Condensed Matter, Physics, TOTAL-ENERGY CALCULATIONS, WAVE BASIS-SET, MOLECULAR-DYNAMICS, DENSITY FUNCTIONALS, RAMAN-SPECTROSCOPY, AMORPHOUS-CARBON, HYDROCARBONS, CHEMISTRY, TRANSPORT, SOLIDS
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Earth Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy
URI: https://discovery.ucl.ac.uk/id/eprint/10044678
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