Machine learning based interatomic potential for amorphous carbon

作者：Deringer, Volker L.^*; Csanyi, Gabor

来源：Physical Review B, 2017, 95(9): 094203.

DOI：10.1103/PhysRevB.95.094203

• DIAMOND-LIKE CARBON
• ELECTRONIC-PROPERTIES
• MOLECULAR-DYNAMICS
• ELASTIC-CONSTANTS
• ENERGY SURFACES
• DENSITY
• FILMS
• PHASE
• DEPOSITION
• STABILITY

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.

• 出版日期2017-3-3