微观溶剂化的统计观点
A statistical perspective on microsolvation
摘要：
传统统计力学的一个弱点是缺乏一个确定与熔池相互作用的小系统的平衡热力学性质的方法。这样一个小系统的典型例子是被显式溶剂化壳包围的溶质。解决这个问题的一种方法是将感兴趣的小系统封闭在显式溶剂分子的大浴中，显式溶剂分子比系统本身大得多。溶剂自由度的显式包含显然受到可用计算资源的限制。一个潜在的补救措施，这个问题是一个微溶剂化的方法，只有几个明确的溶剂分子被认为是由一个隐式溶剂浴包围。尽管如此，溶剂自由度的采样与传统的巨正则蒙特卡罗方法是具有挑战性的，因为没有一个单一的化学势的溶剂分子可以定义在小系统热力学领域。在这项工作中，提出了一种基于巨正则系综的统计热力学模型，该模型避免了传统的系统尺寸限制，并准确地表征了受浴的热力学约束的感兴趣的系统的性质。我们将现有的微溶剂化方法推广到广义的多浴“微观统计”模型，并表明以前导出的方法作为我们的模型的限制。这里描述的框架是通用的，我们验证我们的方法数值Lennard-Jones模型流体。
英文摘要：
The lack of a procedure to determine equilibrium thermodynamic properties of a small system interacting with a bath is frequently seen as a weakness of conventional statistical mechanics. A typical example for such a small system is a solute surrounded by an explicit solvation shell. One way to approach this problem is to enclose the small system of interest in a large bath of explicit solvent molecules, considerably larger than the system itself. The explicit inclusion of the solvent degrees of freedom is obviously limited by the available computational resources. A potential remedy to this problem is a microsolvation approach where only a few explicit solvent molecules are considered and surrounded by an implicit solvent bath. Still, the sampling of the solvent degrees of freedom is challenging with conventional grand canonical Monte Carlo methods, since no single chemical potential for the solvent molecules can be defined in the realm of small-system thermodynamics. In this work, a statistical thermodynamic model based on the grand canonical ensemble is proposed that avoids the conventional system size limitations and accurately characterizes the properties of the system of interest subject to the thermodynamic constraints of the bath. We extend an existing microsolvation approach to a generalized multi-bath "micro-statistical" model and show that the previously derived approaches result as a limit of our model. The framework described here is universal and we validate our method numerically for a Lennard-Jones model fluid.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
