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Machine learning based interatomic potential for amorphous carbon

Accepted version
Peer-reviewed

Type

Article

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Authors

Deringer, VL 

Abstract

We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine learning representation of the density-functional theory (DFT) potential-energy surface, such interatomic potentials enable materials simulations with close-to DFT accuracy but at much lower computational cost. We first determine the maximum accuracy that any finite-range potential can achieve in carbon structures; then, using a hierarchical set of two-, three-, and many-body structural descriptors, we construct a GAP model that can indeed reach the target accuracy. The potential yields accurate energetic and structural properties over a wide range of densities; it also correctly captures the structure of the liquid phases, at variance with a state-of-the-art empirical potential. Exemplary applications of the GAP model to surfaces of “diamondlike” tetrahedral amorphous carbon (ta-C) are presented, including an estimate of the amorphous material’s surface energy and simulations of high-temperature surface reconstructions (“graphitization”). The presented interatomic potential appears to be promising for realistic and accurate simulations of nanoscale amorphous carbon structures.

Description

Keywords

cond-mat.mtrl-sci, cond-mat.mtrl-sci

Journal Title

Physical Review B - Condensed Matter and Materials Physics

Conference Name

Journal ISSN

2469-9950
2469-9969

Volume Title

95

Publisher

American Physical Society
Sponsorship
Engineering and Physical Sciences Research Council (EP/K014560/1)
Isaac Newton Trust (1624(n))
Engineering and Physical Sciences Research Council (EP/P022596/1)
V.L.D. gratefully acknowledges a postdoctoral fellowship from the Alexander von Humboldt Foundation and support from the Isaac Newton Trust (Trinity College Cambridge). This work used the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk) via EPSRC Grant No. EP/K014560/1.
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