The Abdus Salam International Center for Theoretical Physics, Strada Costiera 11, 34151 Trieste, Italy.
SISSA─International School for Advanced Studies, Via Bonomea 265, 34136 Trieste, Italy.
J Chem Theory Comput. 2022 May 10;18(5):3136-3150. doi: 10.1021/acs.jctc.1c01292. Epub 2022 Apr 26.
The microscopic description of the local structure of water remains an open challenge. Here, we adopt an agnostic approach to understanding water's hydrogen bond network using data harvested from molecular dynamics simulations of an empirical water model. A battery of state-of-the-art unsupervised data-science techniques are used to characterize the free-energy landscape of water starting from encoding the water environment using local atomic descriptors, through dimensionality reduction and finally the use of advanced clustering techniques. Analysis of the free energy under ambient conditions was found to be consistent with a rough single basin and independent of the choice of the water model. We find that the fluctuations of the water network occur in a high-dimensional space, which we characterize using a combination of both atomic descriptors and chemical-intuition-based coordinates. We demonstrate that a combination of both types of variables is needed in order to adequately capture the complexity of the fluctuations in the hydrogen bond network at different length scales both at room temperature and also close to the critical point of water. Our results provide a general framework for examining fluctuations in water under different conditions.
水的局部结构的微观描述仍然是一个悬而未决的挑战。在这里，我们采用一种不可知论的方法，使用从经验水模型的分子动力学模拟中收集的数据来理解水的氢键网络。一系列最先进的无监督数据分析技术被用于从使用局部原子描述符对水环境进行编码开始，通过降维和最终使用先进的聚类技术，来描述水的自由能景观。在环境条件下分析自由能的结果与一个粗略的单一盆地且不依赖于水模型的选择一致。我们发现，水网络的波动发生在一个高维空间中，我们使用原子描述符和基于化学直觉的坐标的组合来描述这个空间。我们证明，为了在不同的长度尺度上，无论是在室温下还是在接近水的临界点，都能充分捕捉氢键网络波动的复杂性，需要同时使用这两种类型的变量。我们的结果为在不同条件下检查水的波动提供了一个通用的框架。
