Jian Wang^1,*, Haitao Hei¹, Yonggang Zheng¹, Hongwu Zhang¹, Hongfei Ye¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.010916

Abstract Icing is a ubiquitous phenomenon in nature and widely observed in the micro/nanoconfinement, e.g., two-dimensional ice growth on Au surface, nanoconfinement-induced phase change, nanodroplet freezing on surface, etc. These complicated and abstruse processes and behaviours demand deep understanding from the microscale level by the aid of molecular dynamics (MD) simulation [1]. However, it is still a great challenge to accurately describe the ice and liquid water simultaneously with the present water models [1,2]. In response to this, we propose a series-parallel machine learning (ML) approach consisting of classification back-propagation neural network (BPNN), parallel regression BPNNs… More >

Jian Wang^1,*, Yonggang Zheng¹, Hongwu Zhang¹, Hongfei Ye¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-1, 2023, DOI: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… More >