标题： 机器学习带电胶体在原始1:1电解质中的多体势能 显示英文标题

标题： Machine learning many-body potentials for charged colloids in primitive 1:1 electrolytes

摘要： 带电粒子在电解质中的有效相互作用最常使用Derjaguin-Landau-Verwey-Overbeek(DLVO)势来建模，在该模型中，悬浮液中的离子在平均场层面上被粗粒化处理。 然而，一些实验表明该理论存在不足，因为在强库仑耦合区域（例如低温度、低介电常数、高离子价态、高表面电荷）中，围绕胶体的离子分布由非平凡的相关性所支配。 通过在这些胶体悬浮液的模拟中显式包含离子可以获得见解，尽管直接模拟高度带电球体的分散体系在计算上是具有挑战性的。 为了克服缓慢的平衡问题，我们采用机器学习(ML)框架生成准确描述有效胶体相互作用的ML势。 这些ML势使得快速模拟成为可能，并使得在悬浮液中对带电胶体的大规模模拟成为可能，从而为系统研究它们的相行为（特别是气液和流固共存）提供了可能性。 显示英文摘要

摘要： Effective interactions between charged particles dispersed in an electrolyte are most commonly modeled using the Derjaguin-Landau-Verwey-Overbeek (DLVO) potential, where the ions in the suspension are coarse-grained out at mean-field level. However, several experiments point to shortcomings of this theory, as the distribution of ions surrounding colloids is governed by nontrivial correlations in regimes of strong Coulomb coupling (e.g. low temperature, low dielectric constant, high ion valency, high surface charge). Insight can be gained by explicitly including the ions in simulations of these colloidal suspensions, even though direct simulations of dispersions of highly charged spheres are computationally demanding. To circumvent slow equilibration, we employ a machine-learning (ML) framework to generate ML potentials that accurately describe the effective colloid interactions. These ML potentials enable fast simulations and make large-scale simulations of charged colloids in suspension possible, opening the possibility for a systematic study of their phase behaviour, in particular gas-liquid and fluid-solid coexistence.