A machine learning model to classify dynamic processes in liquid water
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.
Keywords
Cite
@article{arxiv.2104.07965,
title = {A machine learning model to classify dynamic processes in liquid water},
author = {Jie Huang and Gang Huang and Shiben Li},
journal= {arXiv preprint arXiv:2104.07965},
year = {2021}
}
Comments
8 pages, 8 figures, Support Information https://nbviewer.org/github/HuangJiaLian/DataBase0/blob/master/uPic/2021_08_14_09_SI.pdf ; SI Appendix Video 1 (DA exchange) https://nbviewer.org/github/HuangJiaLian/DataBase0/blob/master/uPic/2021_04_19_19_DA1_09_14.gif ; SI Appendix Video 2 (Diffusion) https://nbviewer.org/github/HuangJiaLian/DataBase0/blob/master/uPic/2021_04_19_19_DF2_23_35.gif