TY - JOUR

T1 - Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

AU - Mortazavi, Bohayra

AU - Podryabinkin, Evgeny V.

AU - Novikov, Ivan S.

AU - Roche, Stephan

AU - Rabczuk, Timon

AU - Zhuang, Xiaoying

AU - Shapeev, Alexander V.

N1 - Publisher Copyright:
© 2020 The Author(s). Published by IOP Publishing Ltd.

PY - 2020/4

Y1 - 2020/4

N2 - 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 C3N nanosheets. C3N 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−1 for C3N 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.

AB - 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 C3N nanosheets. C3N 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−1 for C3N 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.

KW - Density functional theory simulations

KW - Machine learning

KW - Molecular dynamics

KW - Thermal conductivity

KW - Two-dimensional polyaniline C3N monolayer

UR - http://www.scopus.com/inward/record.url?scp=85097455821&partnerID=8YFLogxK

U2 - 10.1088/2515-7639/ab7cbb

DO - 10.1088/2515-7639/ab7cbb

M3 - Article

AN - SCOPUS:85097455821

VL - 3

JO - JPhys Materials

JF - JPhys Materials

SN - 2515-7639

IS - 2

M1 - 02LT02

ER -