由机器学习电位揭示的离子对水的异常扩散的结构形成和破坏作用

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL
Jinfeng Liu, Xuchao Zhou, Xiao He
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引用次数: 0

摘要

水的动力学在离子的溶剂化中表现出反常的行为,而离子对水的氢键结构的扰动的理解仍然是一个悬而未决的问题。在这项研究中,我们利用MP2水平的理论开发的深度机器学习电位研究了水在NaCl和CsI水溶液中的异常扩散行为及其分子起源。我们的模拟表明,在不同的盐浓度下,NaCl溶液中水的扩散受到抑制,而CsI溶液中水的扩散则得到增强,完美地再现了实验观察结果。我们通过研究水氢键网络的结构变化,以及水分子的振动特性,进一步探讨了这种异常扩散行为的微观起源。我们的研究结果强调了不同离子在盐溶液中调节水的结构和动态行为中所起的不同作用,并为控制水异常扩散的结构形成和破坏的基本机制提供了关键见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structure making and breaking effects of ions on the anomalous diffusion of water revealed by machine learning potentials
The dynamics of water exhibits anomalous behavior in the solvation of ions, and understanding the perturbation that ions make on the hydrogen bond structure of water remains an open question. In this study, we investigate the anomalous diffusion behavior of water and its molecular origins in aqueous NaCl and CsI solutions using deep machine learning potentials developed at the MP2 level of theory. Our simulations reveal a suppression of water diffusion in NaCl solutions and whereas an enhancement of water diffusion in CsI solutions across various salt concentrations, perfectly reproducing the experimental observations. We further explore the microscopic origins of this anomalous diffusion behavior by examining the structural changes of water hydrogen bond network, as well as the vibrational properties of water molecules. Our findings highlight the distinct roles that different ions play in modulating the structure and dynamic behavior of water in salt solutions, and provide key insights into the fundamental mechanisms of structure making and breaking that govern the anomalous diffusion of water.
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来源期刊
Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics 化学-物理:原子、分子和化学物理
CiteScore
5.50
自引率
9.10%
发文量
2675
审稿时长
2.0 months
期刊介绍: Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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