Physically inspired atom-centered symmetry functions for the construction of high dimensional neural network potential energy surfaces
文献类型：期刊论文
| 作者 | Zhang, Kangyu1,2; Yin, Lichang1,2; Liu, Gang1,2 |
| 刊名 | COMPUTATIONAL MATERIALS SCIENCE
 |
| 出版日期 | 2021 |
| 卷号 | 186页码:7 |
| 关键词 | Machine learning Potential energy surface Atom centered symmetry function Solid electrolyte Molecular dynamics simulation |
| ISSN号 | 0927-0256 |
| DOI | 10.1016/j.commatsci.2020.110071 |
| 通讯作者 | Yin, Lichang(lcyin@imr.ac.cn) |
| 英文摘要 | Among 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. |
| 资助项目 | National Natural Science Foundation of China (NSFC)[51972312] ; National Natural Science Foundation of China (NSFC)[51472249] |
| WOS研究方向 | Materials Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000594489800005 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.imr.ac.cn/handle/321006/158752]  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 通讯作者 | Yin, Lichang |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：金属研究所
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