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Physically inspired atom-centered symmetry functions for the construction of high dimensional neural network potential energy surfaces
Zhang, Kangyu1,2; Yin, Lichang1,2; Liu, Gang1,2
Corresponding AuthorYin, Lichang(lcyin@imr.ac.cn)
2021
Source PublicationCOMPUTATIONAL MATERIALS SCIENCE
ISSN0927-0256
Volume186Pages:7
AbstractAmong different atomistic neural network (AtNN) potential energy surfaces (PESs), the Behler-Parrinello neural network (BPNN) based on atom-centered symmetry functions (ACSFs) has been proved to be capable of constructing accurate PESs for various crystals. A judicious setting of the parameters of the ACSFs largely determines the accuracy of a BPNN PES. However, this is typically an ad hoc and tedious task requiring highly acute chemical intuition. To address this issue, we derived a set of physically inspired ACSFs from the effective densities of atoms, in which the radii of atoms are naturally incorporated. Therefore, the parameters of the physically inspired ACSFs can be directly chosen based on the types of chemical bonds within a target system. Compared with the original ones, the physically inspired ACSFs are more suitable for complex systems based on its better performance on predicting the formation enthalpies of molecules in QM9 database. Moreover, the physically inspired ACSFs can also effectively accelerate the convergence of the atomic forces during the training of an AtNN PES. With the physically inspired ACSFs, we constructed a highly accurate AtNN PES for a solid electrolyte Li10GeP2S12. Based on the AtNN PES, we studied the bulk Li ion diffusion within Li10GeP2S12 by molecular dynamics (MD) simulations. The MD results well reproduced the experimental results, indicating the high accuracy of the AtNN PES constructed with the physically inspired ACSFs.
KeywordMachine learning Potential energy surface Atom centered symmetry function Solid electrolyte Molecular dynamics simulation
Funding OrganizationNational Natural Science Foundation of China (NSFC)
DOI10.1016/j.commatsci.2020.110071
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[51972312] ; National Natural Science Foundation of China (NSFC)[51472249]
WOS Research AreaMaterials Science
WOS SubjectMaterials Science, Multidisciplinary
WOS IDWOS:000594489800005
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/158753
Collection中国科学院金属研究所
Corresponding AuthorYin, Lichang
Affiliation1.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, 72 Wenhua Rd, Shenyang 110016, Peoples R China
2.Univ Sci & Technol China, Sch Mat Sci & Engn, 72 Wenhua Rd, Shenyang 110016, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Kangyu,Yin, Lichang,Liu, Gang. Physically inspired atom-centered symmetry functions for the construction of high dimensional neural network potential energy surfaces[J]. COMPUTATIONAL MATERIALS SCIENCE,2021,186:7.
APA Zhang, Kangyu,Yin, Lichang,&Liu, Gang.(2021).Physically inspired atom-centered symmetry functions for the construction of high dimensional neural network potential energy surfaces.COMPUTATIONAL MATERIALS SCIENCE,186,7.
MLA Zhang, Kangyu,et al."Physically inspired atom-centered symmetry functions for the construction of high dimensional neural network potential energy surfaces".COMPUTATIONAL MATERIALS SCIENCE 186(2021):7.
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