TY - JOUR

T1 - Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials

T2 - A MTP/ShengBTE solution

AU - Mortazavi, Bohayra

AU - Podryabinkin, Evgeny V.

AU - Novikov, Ivan S.

AU - Rabczuk, Timon

AU - Zhuang, Xiaoying

AU - Shapeev, Alexander V.

N1 - Funding Information: B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). E.V.P, I.S.N., and A.V.S. were supported by the Russian Science Foundation (Grant No. 18-13-00479 ).

PY - 2021/1

Y1 - 2021/1

N2 - Accurate evaluation of the thermal conductivity of a material can be a challenging task from both experimental and theoretical points of view. In particular for the nanostructured materials, the experimental measurement of thermal conductivity is associated with diverse sources of uncertainty. As a viable alternative to experiment, the combination of density functional theory (DFT) simulations and the solution of Boltzmann transport equation is currently considered as the most trusted approach to examine thermal conductivity. The main bottleneck of the aforementioned method is to acquire the anharmonic interatomic force constants using the computationally demanding DFT calculations. In this work we propose a substantially accelerated approach for the evaluation of anharmonic interatomic force constants via employing machine-learning interatomic potentials (MLIPs) trained over short ab initio molecular dynamics trajectories. The remarkable accuracy of the proposed accelerated method is confirmed by comparing the estimated thermal conductivities of several bulk and two-dimensional materials with those computed by the full-DFT approach. The MLIP-based method proposed in this study can be employed as a standard tool, which would substantially accelerate and facilitate the estimation of lattice thermal conductivity in comparison with the commonly used full-DFT solution.

AB - Accurate evaluation of the thermal conductivity of a material can be a challenging task from both experimental and theoretical points of view. In particular for the nanostructured materials, the experimental measurement of thermal conductivity is associated with diverse sources of uncertainty. As a viable alternative to experiment, the combination of density functional theory (DFT) simulations and the solution of Boltzmann transport equation is currently considered as the most trusted approach to examine thermal conductivity. The main bottleneck of the aforementioned method is to acquire the anharmonic interatomic force constants using the computationally demanding DFT calculations. In this work we propose a substantially accelerated approach for the evaluation of anharmonic interatomic force constants via employing machine-learning interatomic potentials (MLIPs) trained over short ab initio molecular dynamics trajectories. The remarkable accuracy of the proposed accelerated method is confirmed by comparing the estimated thermal conductivities of several bulk and two-dimensional materials with those computed by the full-DFT approach. The MLIP-based method proposed in this study can be employed as a standard tool, which would substantially accelerate and facilitate the estimation of lattice thermal conductivity in comparison with the commonly used full-DFT solution.

KW - 2D materials

KW - First-principles

KW - Interatomic potentials

KW - Machine-learning

KW - Thermal conductivity

UR - http://www.scopus.com/inward/record.url?scp=85090322773&partnerID=8YFLogxK

U2 - 10.1016/j.cpc.2020.107583

DO - 10.1016/j.cpc.2020.107583

M3 - Article

AN - SCOPUS:85090322773

VL - 258

JO - Computer physics communications

JF - Computer physics communications

SN - 0010-4655

M1 - 107583

ER -