Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials
Mortazavi, Bohayra (Leibniz Universität Hannover. Institute of Continuum Mechanics)
Podryabinkin, Evgeny V. (Skolkovo Innovation Center. Skolkovo Institute of Science and Technology)
Novikov, Ivan S. (Skolkovo Innovation Center. Skolkovo Institute of Science and Technology)
Roche, Stephan (Institut Català de Nanociència i Nanotecnologia)
Rabczuk, Timon (Tongji University. Department of Geotechnical Engineering)
Zhuang, Xiaoying (Leibniz Universität Hannover. Institute of Continuum Mechanics)
Shapeev, Alexander V. (Skolkovo Innovation Center. Skolkovo Institute of Science and Technology)

Date:	2020
Abstract:	It is well-known that the calculation of thermal conductivity using classical molecular dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic potentials. As proven for the case of graphene, while most of the available interatomic potentials estimate the structural and elastic constants with high accuracy, when employed to predict the lattice thermal conductivity they however lead to a variation of predictions by one order of magnitude. Here we present our results on using machine-learning interatomic potentials (MLIPs) passively fitted to computationally inexpensive ab-initio molecular dynamics trajectories without any tuning or optimizing of hyperparameters. These first-attempt potentials could reproduce the phononic properties of different two-dimensional (2D) materials obtained using density functional theory (DFT) simulations. To illustrate the efficiency of the trained MLIPs, we consider polyaniline CN nanosheets. CN monolayer was selected because the classical MD and different first-principles results contradict each other, resulting in a scientific dilemma. It is shown that the predicted thermal conductivity of 418 ± 20 W mK for CN monolayer by the non-equilibrium MD simulations on the basis of a first-attempt MLIP evidences an improved accuracy when compared with the commonly employed MD models. Moreover, MLIP-based prediction can be considered as a solution to the debated reports in the literature. This study highlights that passively fitted MLIPs can be effectively employed as versatile and efficient tools to obtain accurate estimations of thermal conductivities of complex materials using classical MD simulations. In response to remarkable growth of 2D materials family, the devised modeling methodology could play a fundamental role to predict the thermal conductivity.
Grants:	Ministerio de Economía y Competitividad SEV-2017-0706
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Machine learning ; Thermal conductivity ; Molecular dynamics ; Density functional theory simulations ; Two-dimensional polyaniline C3N monolayer
Published in:	JPhys materials, Vol. 3, Issue 2 (April 2020) , art. 2LT02, ISSN 2515-7639

DOI: 10.1088/2515-7639/ab7cbb

11 p, 2.1 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Experimental sciences > Catalan Institute of Nanoscience and Nanotechnology (ICN2)
Articles > Research articles
Articles > Published articles