Machine learning based interatomic potential for amorphous carbon

@article{Deringer2017MachineLB,
  title={Machine learning based interatomic potential for amorphous carbon},
  author={Volker L. Deringer and G{\'a}bor Cs{\'a}nyi},
  journal={Physical Review B},
  year={2017},
  volume={95},
  pages={094203}
}
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… 
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