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Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures / Mortazavi, Bohayra (Leibniz Universität Hannover. Department of Mathematics and Physics) ; Podryabinkin, Evgeny V. (Skolkovo Innovation Center) ; Roche, Stephan (Institut Català de Nanociència i Nanotecnologia) ; Rabczuk, Timon (Tongji University. Department of Geotechnical Engineering) ; Zhuang, Xiaoying (Leibniz Universität Hannover. Department of Mathematics and Physics) ; Shapeev, Alexander V. (Skolkovo Institute of Science and Technology)
One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as density functional theory (DFT) provide the best possible accuracy on electronic properties but they are limited to systems up to a few hundreds, or at most thousands of atoms. [...]
2020 - DOI (Digital Object Identifier)

10.1039/d0mh00787k
Materials Horizons, Vol. 7, issue 9 (2020) , p. 2359-2367

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