Machine Learning Interatomic Potentials Enable First-Principles Multiscale Modeling of Lattice Thermal Conductivity in Graphene/Borophene Heterostructures

Published: 10 May 2020| Version 1 | DOI: 10.17632/pbgscy3ptg.1
Contributors:
Bohayra Mortazavi, Alexander V. Shapeev, Evgeny V. Podryabinkin

Description

Data for manuscript entitled: "Machine Learning Interatomic Potentials Enable First-Principles Multiscale Modeling of Lattice Thermal Conductivity in Graphene/Borophene Heterostructures". Please find the "Supporting information.pdf" for more details.

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Institutions

  • Skolkovo Institute of Science and Technology
  • Leibniz Universitat Hannover

Categories

Machine Learning, Density Functional Theory, Molecular Dynamics, Thermal Conductivity, Multiscale Analysis, Two-Dimensional Material

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