A high-accuracy machine-learning water model for exploring water nanocluster structures
文献类型：期刊论文
| 作者 | Zhou, Hao1 ; Feng, Ya-Juan1; Wang, Chao3; Huang, Teng4; Liu, Yi-Rong1; Jiang, Shuai1; Wang, Chun-Yu1; Huang, Wei1,2,4
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| 刊名 | NANOSCALE
 |
| 出版日期 | 2021-06-14 |
| ISSN号 | 2040-3364 |
| DOI | 10.1039/d1nr03128g |
| 通讯作者 | Feng, Ya-Juan(fengyj6@ustc.edu.cn) ; Huang, Wei(huangwei6@ustc.edu.cn) |
| 英文摘要 | Water, the most important molecule on the Earth, possesses many essential and unique physical properties that are far from completely understood, partly due to serious difficulties in identifying the precise microscopic structures of water. Hence, identifying the structures of water nanoclusters is a fundamental and challenging issue for studies on the relationship between the macroscopic physical properties of water and its microscopic structures. For large-scale simulations (at the level of nm and ns) of water nanoclusters, a calculation method with simultaneous accuracy at the level of quantum chemistry and efficiency at the level of an empirical potential method is in great demand. Herein, a machine-learning (ML) water model was utilized to explore the microscopic structural features at different length scales for water nanoclusters with a size up to several nm. The ML water model can be employed to efficiently predict the structures of water nanoclusters with a similar accuracy to that of density functional theory and with substantially lower computational resource demands. To validate the low-lying structure search results with experimental spectral results, an ML water model combined with velocity autocorrelation function analysis was used to simulate the vibrational spectra of water nanoclusters with up to thousands of water molecules. By comparing the simulated and experimentally recorded vibrational spectra, the atomic structures determined by a simulation based on the ML water model are all verified. To demonstrate its ability to represent water's structural evolution at large length and time scales, the ML water model was employed to model the structural evolution during the crystal-liquid transition, and the phase transition temperatures of water clusters with different sizes were precisely predicted. The ML water model provides an efficient theoretical calculation tool for exploring the structures and physical properties of water and their relationships, especially for clusters with relatively large sizes and processes with relatively long durations. |
| WOS关键词 | DENSITY FUNCTIONALS ; X-RAY ; SIZE ; ICE ; CRYSTALLIZATION ; SPECTROSCOPY ; SPECTRA ; LIQUID ; ENERGIES ; CLUSTERS |
| 资助项目 | National Natural Science Foundation of China[41877305] ; National Natural Science Foundation of China[41775112] ; National Science Fund for Distinguished Young Scholars[41725019] ; Fundamental Research Funds for the Central Universities[WK2310000103] |
| WOS研究方向 | Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000670334700001 |
| 出版者 | ROYAL SOC CHEMISTRY |
| 资助机构 | National Natural Science Foundation of China ; National Science Fund for Distinguished Young Scholars ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/123427]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Feng, Ya-Juan; Huang, Wei |
| 作者单位 | 1.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230026, Anhui, Peoples R China 2.CAS Ctr Excellent Urban Atmospher Environm, Xiamen 361021, Fujian, Peoples R China 3.Univ Sci & Technol China, Natl Synchrotron Radiat Lab, Hefei 230026, Anhui, Peoples R China 4.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Lab Atmospher Physicochem, Hefei 230031, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Hao,Feng, Ya-Juan,Wang, Chao,et al. A high-accuracy machine-learning water model for exploring water nanocluster structures[J]. NANOSCALE,2021. |
| APA | Zhou, Hao.,Feng, Ya-Juan.,Wang, Chao.,Huang, Teng.,Liu, Yi-Rong.,...&Huang, Wei.(2021).A high-accuracy machine-learning water model for exploring water nanocluster structures.NANOSCALE. |
| MLA | Zhou, Hao,et al."A high-accuracy machine-learning water model for exploring water nanocluster structures".NANOSCALE (2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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