Wang Jian, Hei Haitao, Zheng Yonggang, Zhang Hongwu, Ye Hongfei
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, P. R. China.
DUT-BSU Joint Institute, Dalian University of Technology, Dalian 116024, P. R. China.
J Chem Theory Comput. 2024 Sep 10;20(17):7533-7545. doi: 10.1021/acs.jctc.4c00440. Epub 2024 Aug 12.
Icing, a common natural phenomenon, always originates from a molecule. Molecular simulation is crucial for understanding the relevant process but still faces a great challenge in obtaining a uniform and accurate description of ice and liquid water with limited model parameters. Here, we propose a series-parallel machine learning (ML) approach consisting of a classification back-propagation neural network (BPNN), parallel regression BPNNs, and a genetic algorithm to establish conventional TIP5P-BG and temperature-dependent TIP5P-BGT models. The established water models exhibit a comprehensive balance among the crucial physical properties (melting point, density, vaporization enthalpy, self-diffusion coefficient, and viscosity) with mean absolute percentage errors of 2.65 and 2.40%, respectively, and excellent predictive performance on the related properties of liquid water. For ice, the simulation results on the critical nucleus size and growth rate are in good accordance with experiments. This work offers a powerful molecular model for phase transition and icing in nanoconfinement and a construction strategy for a complex molecular model in the extreme case.
结冰是一种常见的自然现象，总是起源于一个分子。分子模拟对于理解相关过程至关重要，但在用有限的模型参数获得对冰和液态水的统一且准确的描述方面仍面临巨大挑战。在此，我们提出一种串并行机器学习（ML）方法，该方法由分类反向传播神经网络（BPNN）、并行回归BPNN和遗传算法组成，以建立传统的TIP5P - BG模型和温度依赖的TIP5P - BGT模型。所建立的水模型在关键物理性质（熔点、密度、汽化焓、自扩散系数和粘度）之间展现出全面的平衡，平均绝对百分比误差分别为2.65%和2.40%，并且对液态水的相关性质具有出色的预测性能。对于冰，在临界核尺寸和生长速率方面的模拟结果与实验结果高度吻合。这项工作为纳米限域中的相变和结冰提供了一个强大的分子模型，以及在极端情况下构建复杂分子模型的策略。
