论文标题
贝叶斯无监督的学习揭示了浓缩电解质中隐藏的结构
Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes
论文作者
论文摘要
电解质在从储能到生物材料的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神秘。许多理论方法试图通过引入离子对的概念来对浓缩电解质进行建模，而离子要么与反离子配对，要么与反离子配对，要么“免费”以筛选电荷。在这项研究中，我们将问题重新描述为计算统计的语言，并测试所有离子共享相同本地环境的零假设。将框架应用于分子动力学模拟，我们表明该零假设不受数据的支持。我们的统计技术表明存在不同的局部离子环境。令人惊讶的是，这些差异在类似的电荷相关性中出现，而不是与电荷吸引力不同。非聚集环境中粒子的所得部分显示出不同背景介电常数和离子浓度的普遍缩放行为。
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 electrolytes 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 show that this null hypothesis is not supported by data. Our statistical technique suggests the presence of distinct local ionic environments; surprisingly, these differences arise in like charge correlations rather than unlike charge attraction. The resulting fraction of particles in non-aggregated environments shows a universal scaling behaviour across different background dielectric constants and ionic concentrations.
