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A Four-Site Water Model for Liquid and Supercooled Water Based on Machine Learning: TIP4P-BGWT
Jian Wang1,*, Yonggang Zheng1, Hongwu Zhang1, Hongfei Ye1
1 International Research Center for Computational Mechanics, State Key Laboratory of Structural Analysis, Optimization
and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and
Mechanics, Dalian University of Technology, Dalian, 116024, China
* Corresponding Author: Jian Wang. Email:
The International Conference on Computational & Experimental Engineering and Sciences 2023, 27(3), 1-1. https://doi.org/10.32604/icces.2023.09761
Abstract
Water is the most ubiquitous fluid in nature and widely exists in the micro/nanoconfinement of leafstalks,
shale, bones, etc. The complex relation of the properties and behaviours of water to the temperature,
pressure and confinement size enhances the difficulty in the accurate simulation, such as the supercooled
state of pure water below the freezing point. As a powerful tool, molecular dynamics simulation is adequate
for investigating the relevant properties and behaviours. However, accurately calculating the physical
properties of liquid and supercooled water is quite challenging by molecular simulations owing to limited
model parameters. Machine learning (ML) techniques and temperature-dependent parameters provide a
path to efficiently reparametrize TIP4P model. Here, 7000 molecular dynamics (MD) samples for liquid and
supercooled water from 253 K to 373 K are generated to train the back-propagation neural network with
better generalization ability. This network could rapidly and accurately provide adequate data for genetic
algorithm to reparametrize the molecular model without extra time-consuming molecular simulations.
Based on the proposed optimized approach, a water model TIP4P-BGWT with temperature-dependent
model parameters is established. It exhibits excellent predictive performance with comprehensive balance
for the four crucial physical properties (density, vaporization enthalpy, self-diffusion coefficient and
viscosity) of water. The corresponding mean absolute percentage error is 3.26%, which is lower than the
existing water models. Furthermore, the calculated results of the temperature of maximum density, thermal
expansion coefficient, isothermal compressibility, surface tension, radial distribution function and the
average number of hydrogen bonds per molecule are also in good consistent with experiments. It is notable
that the established water model exhibits excellent performance for the supercooled water. The present
study offers an accurate numerical model of liquid and supercooled water for the molecular simulationbased researches on the nanoflow, nanodroplet and interfacial fluids, etc.
Keywords
Cite This Article
APA Style
Wang, J., Zheng, Y., Zhang, H., Ye, H. (2023). A four-site water model for liquid and supercooled water based on machine learning: TIP4P-BGWT. The International Conference on Computational & Experimental Engineering and Sciences, 27(3), 1-1. https://doi.org/10.32604/icces.2023.09761
Vancouver Style
Wang J, Zheng Y, Zhang H, Ye H. A four-site water model for liquid and supercooled water based on machine learning: TIP4P-BGWT. Int Conf Comput Exp Eng Sciences . 2023;27(3):1-1 https://doi.org/10.32604/icces.2023.09761
IEEE Style
J. Wang, Y. Zheng, H. Zhang, and H. Ye "A Four-Site Water Model for Liquid and Supercooled Water Based on Machine Learning: TIP4P-BGWT," Int. Conf. Comput. Exp. Eng. Sciences , vol. 27, no. 3, pp. 1-1. 2023. https://doi.org/10.32604/icces.2023.09761