Jian Wang1,*, 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 >