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贝叶斯无监督学习揭示了浓缩电解质中的隐藏结构。

Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes.

机构信息

Department of Physics, University of Cambridge, CB3 0HE Cambridge, United Kingdom.

Institute of Physics, University of Freiburg, Hermann-Herder-Straße 3, 79104 Freiburg im Breisgau, Germany.

出版信息

J Chem Phys. 2021 Apr 7;154(13):134902. doi: 10.1063/5.0039617.

DOI:10.1063/5.0039617
PMID:33832269
Abstract

Electrolytes play an important role in a plethora of applications ranging from energy storage to biomaterials. Notwithstanding this, the structure of concentrated electrolytes remains enigmatic. Many theoretical approaches attempt to model the concentrated electrolyte by introducing the idea of ion pairs, with ions either being tightly "paired" with a counter-ion or "free" to screen charge. In this study, we reframe the problem into the language of computational statistics and test the null hypothesis that all ions share the same local environment. Applying the framework to molecular dynamics simulations, we find that this null hypothesis is not supported by data. Our statistical technique suggests the presence of two distinct local ionic environments at intermediate concentrations, whose differences surprisingly originate in like charge correlations rather than unlike charge attraction. Through considering the effect of these "aggregated" and "non-aggregated" states on bulk properties including effective ion concentration and dielectric constant, we identify a scaling relation between the effective screening length and theoretical Debye length, which applies across different dielectric constants and ion concentrations.

摘要

电解质在从储能到生物材料等众多应用中起着重要作用。尽管如此,浓缩电解质的结构仍然很神秘。许多理论方法试图通过引入离子对的概念来模拟浓缩电解质,其中离子要么与反离子紧密“配对”,要么自由屏蔽电荷。在这项研究中,我们将问题重新表述为计算统计学的语言,并检验所有离子具有相同局部环境的零假设。通过将该框架应用于分子动力学模拟,我们发现数据不支持该零假设。我们的统计技术表明,在中等浓度下存在两种不同的局部离子环境,其差异出人意料地源于同电荷相关性,而不是异电荷吸引力。通过考虑这些“聚集”和“非聚集”状态对包括有效离子浓度和介电常数在内的体性质的影响,我们确定了有效屏蔽长度与理论德拜长度之间的标度关系,该关系适用于不同的介电常数和离子浓度。

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Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes.贝叶斯无监督学习揭示了浓缩电解质中的隐藏结构。
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Underscreening and hidden ion structures in large scale simulations of concentrated electrolytes.浓电解质大规模模拟中的欠筛选与隐藏离子结构
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Colloidal Systems in Concentrated Electrolyte Solutions Exhibit Re-entrant Long-Range Electrostatic Interactions due to Underscreening.由于屏蔽不足,浓电解质溶液中的胶体系统表现出折返式长程静电相互作用。
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