Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica.

@article{Kobayashi2021SelflearningHM,
  title={Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica.},
  author={Keita Kobayashi and Yuki Nagai and Mitsuhiro Itakura and Motoyuki Shiga},
  journal={The Journal of chemical physics},
  year={2021},
  volume={155 3},
  pages={
          034106
        }
}
  • Keita Kobayashi, Y. Nagai, +1 author M. Shiga
  • Published 2021
  • Medicine, Physics
  • The Journal of chemical physics
Self-learning hybrid Monte Carlo (SLHMC) is a first-principles simulation that allows for exact ensemble generation on potential energy surfaces based on density functional theory. The statistical sampling can be accelerated with the assistance of smart trial moves by machine learning potentials. In the first report [Nagai et al., Phys. Rev. B 102, 041124(R) (2020)], the SLHMC approach was introduced for the simplest case of canonical sampling. We herein extend this idea to isothermal-isobaric… Expand

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