Atomic potential energy uncertainty in machine-learning interatomic potentials and thermal transport in solids with atomic diffusion
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.
NI Journal Papers
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]
|WOS Research Area|
Materials Science ; Physics
Materials Science, Multidisciplinary ; Physics, Applied ; Physics, Condensed Matter
|WOS Accession No|
|ESI Research Field|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Materials Science and Engineering|
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
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
|First Author Affilication||Department of Materials Science and Engineering|
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).
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