Desgranges Caroline, Delhommelle Jerome
MetaSimulation of Nonequilibrium Processes (MSNEP), Tech Accelerator, University of North Dakota, Grand Forks, North Dakota 58202, USA.
J Chem Phys. 2022 Feb 28;156(8):084113. doi: 10.1063/5.0083458.
The entropy change that occurs upon mixing two fluids has remained an intriguing topic since the dawn of statistical mechanics. In this work, we generalize the grand-isobaric ensemble to mixtures and develop a Monte Carlo algorithm for the rapid determination of entropy in these systems. A key advantage of adiabatic ensembles is the direct connection they provide with entropy. Here, we show how the entropy of a binary mixture A-B can be readily obtained in the adiabatic grand-isobaric (μ, μ, P, R) ensemble, in which μ and μ denote the chemical potential of components A and B, respectively, P is the pressure, and R is the heat (Ray) function, that corresponds to the total energy of the system. This, in turn, allows for the evaluation of the entropy of mixing and the Gibbs free energy of mixing. We also demonstrate that our approach performs very well both on systems modeled with simple potentials and with complex many-body force fields. Finally, this approach provides a direct route to the determination of the thermodynamic properties of mixing and allows for the efficient detection of departures from ideal behavior in mixtures.
自统计力学诞生以来，两种流体混合时发生的熵变一直是一个引人入胜的话题。在这项工作中，我们将巨等压系综推广到混合物，并开发了一种蒙特卡罗算法，用于快速确定这些系统中的熵。绝热系综的一个关键优势是它们与熵直接相关。在这里，我们展示了如何在绝热巨等压（μ，μ，P，R）系综中轻松获得二元混合物A - B的熵，其中μ和μ分别表示组分A和B的化学势，P是压力，R是热（瑞）函数，它对应于系统的总能量。这反过来又允许评估混合熵和混合吉布斯自由能。我们还证明了我们的方法在具有简单势模型的系统和复杂多体力场的系统上都表现得非常好。最后，这种方法提供了一条直接确定混合热力学性质的途径，并允许有效检测混合物中偏离理想行为的情况。
