Atomic potential energy uncertainty in machine-learning interatomic potentials and thermal transport in solids with atomic diffusion

Zhu，Yifan1,2,3; Dong，Erting3,4; Yang，Hongliang1,2,3; Xi，Lili5,6; Yang，Jiong5,6; Zhang，Wenqing1,3,7

2023-07-01

10.1103/PhysRevB.108.014108

Physical Review B   影响因子和分区

2469-9950

2469-9969

Thermal transport simulations have attracted wide attention in recent years, and one standard approach is to use the Green-Kubo method based on machine-learning interatomic potentials and equilibrium molecular dynamics (GK-MLIP-EMD). In this work, we focus on the lattice thermal conductivities κLs for solids with atomic diffusion by taking β-Cu2-xSe (0≤x≤0.05) as an example. Surprisingly, the GK-MLIP-EMD approach fails in the evaluation of κLs for β-Cu1.95Se, whereas the direct method based on nonequilibrium molecular dynamics reliably predicts these values instead. The failure of GK-MLIP-EMD for β-Cu1.95Se could be attributed to the ambiguous projection of the local atomic potential energy Ui in MLIPs, exacerbated by the Cu diffusion at elevated temperatures. The Cu diffusion in β-Cu1.95Se greatly increases the ratio of the convective term and the uncertainty of the conductive term. These influences are considered negligible in crystalline solids. Our findings imply that the ambiguous definition of Ui in MLIPs breaks down the applicability of the GK-MLIP-EMD approach to κL prediction for solids with severe atomic diffusion.

SCI ; EI

英语

其他

National Natural Science Foundation of China["52172216","92163212"] ; Key Research Project of Zhejiang Laboratory[2021PE0AC02] ; Guangdong Innovation Research Team Project[2017ZT07C062] ; Shenzhen Municipal Key -Lab program[ZDSYS20190902092905285] ; Guangdong Provincial Key-Lab program[2019B030301001] ; Guangdong Major Talent Project Introduction Category[2019CX01C237] ; null[SUSTech]

Materials Science ; Physics

Materials Science, Multidisciplinary ; Physics, Applied ; Physics, Condensed Matter

WOS:001061332600001

AMER PHYSICAL SOC

20233214512136

Atoms ; Diffusion in liquids ; Diffusion in solids ; Molecular dynamics ; Molecular physics ; Potential energy ; Thermal conductivity

Thermodynamics:641.1 ; Artificial Intelligence:723.4 ; Physical Chemistry:801.4 ; Atomic and Molecular Physics:931.3

PHYSICS

2-s2.0-85166974234

Scopus

1.State Key Laboratory of High Performance Ceramics and Superfine Microstructure,Shanghai Institute of Ceramics,Chinese Academy of Sciences,Shanghai,200050,China
2.University of Chinese Academy of Sciences,Beijing,100049,China
3.Department of Materials Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
4.College of Materials Science and Engineering,Henan Institute of Technology,Xinxiang,Henan,453000,China
5.Materials Genome Institute,Shanghai University,Shanghai,200444,China
6.Zhejiang Laboratory,Hangzhou,Zhejiang,311100,China
7.Shenzhen Municipal Key-Lab for Advanced Quantum Materials and Devices,Guangdong Provincial Key Lab for Computational Science and Materials Design,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China

Zhu，Yifan,Dong，Erting,Yang，Hongliang,et al. Atomic potential energy uncertainty in machine-learning interatomic potentials and thermal transport in solids with atomic diffusion[J]. Physical Review B,2023,108(1).

Zhu，Yifan,Dong，Erting,Yang，Hongliang,Xi，Lili,Yang，Jiong,&Zhang，Wenqing.(2023).Atomic potential energy uncertainty in machine-learning interatomic potentials and thermal transport in solids with atomic diffusion.Physical Review B,108(1).

Zhu，Yifan,et al."Atomic potential energy uncertainty in machine-learning interatomic potentials and thermal transport in solids with atomic diffusion".Physical Review B 108.1(2023).