A Machine Learning Model to Classify Dynamic Processes in Liquid Water**

doi:10.1002/cphc.202100599

ITP OpenIR > SCI期刊论文

	Huang, Jie ; Huang, Gang 1; Li, Shiben
	A Machine Learning Model to Classify Dynamic Processes in Liquid Water**
Source Publication	CHEMPHYSCHEM
Language	英语
Keyword	JUMP MECHANISM CLUSTERS EXCHANGE SPECTROSCOPY DIFFUSION NETWORKS CELL
Abstract	The dynamics of water molecules plays a vital role in understanding water. We combined computer simulation and deep learning to study the dynamics of H-bonds between water molecules. Based on ab initio molecular dynamics simulations and a newly defined directed Hydrogen (H-) bond population operator, we studied a typical dynamic process in bulk water: interchange, in which the H-bond donor reverses roles with the acceptor. By designing a recurrent neural network-based model, we have successfully classified the interchange and breakage processes in water. We have found that the ratio between them is approximately 1 : 4, and it hardly depends on temperatures from 280 to 360 K. This work implies that deep learning has the great potential to help distinguish complex dynamic processes containing H-bonds in other systems.
	2022
ISSN	1439-4235
Cooperation Status	国内
Subject Area	Chemistry ; Physics
MOST Discipline Catalogue	Chemistry, Physical ; Physics, Atomic, Molecular & Chemical
DOI	10.1002/cphc.202100599
Indexed By	SCIE
Citation statistics
Document Type	期刊论文
Identifier	http://ir.itp.ac.cn/handle/311006/27909
Collection	SCI期刊论文
Affiliation	1.Wenzhou Univ, Dept Phys, Wenzhou 325035, Zhejiang, Peoples R China 2.Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China
Recommended Citation GB/T 7714	Huang, Jie,Huang, Gang,Li, Shiben. A Machine Learning Model to Classify Dynamic Processes in Liquid Water**[J]. CHEMPHYSCHEM,2022.
APA	Huang, Jie,Huang, Gang,&Li, Shiben.(2022).A Machine Learning Model to Classify Dynamic Processes in Liquid Water**.CHEMPHYSCHEM.
MLA	Huang, Jie,et al."A Machine Learning Model to Classify Dynamic Processes in Liquid Water**".CHEMPHYSCHEM (2022).

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