Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures

Mortazavi, Bohayra; Podryabinkin, Evgeny V; Roche, Stephan; Rabczuk, Timon; Zhuang, Xiaoying; Shapeev, Alexander V

by Bohayra Mortazavi, Evgeny V Podryabinkin, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev

Reference:

Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures (Bohayra Mortazavi, Evgeny V Podryabinkin, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev), Materials Horizons, Royal Society of Chemistry, volume 7, 2020.

Bibtex Entry:

@article{mortazavi2020machine,
  title={Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures},
  author={Mortazavi, Bohayra and Podryabinkin, Evgeny V and Roche, Stephan and Rabczuk, Timon and Zhuang, Xiaoying and Shapeev, Alexander V},
  journal={Materials Horizons},
  volume={7},
  number={9},
  pages={2359--2367},
  year={2020},
  publisher={Royal Society of Chemistry}
}