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Machine learning based interatomic potential for amorphous carbon
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DOI：10.1103/PhysRevB.95.094203.png)
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Abstract
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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) potentialenergy 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.
Keywords:
DIAMOND-LIKE CARBON
ELECTRONIC-PROPERTIES
MOLECULAR-DYNAMICS
ELASTIC-CONSTANTS
ENERGY SURFACES
DENSITY
FILMS
PHASE
DEPOSITION
STABILITY
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