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基于量子团簇平衡理论中修正的配分函数计算改进的汽化焓和熵

Calculation of improved enthalpy and entropy of vaporization by a modified partition function in quantum cluster equilibrium theory.

作者信息

Zaby Paul, Ingenmey Johannes, Kirchner Barbara, Grimme Stefan, Ehlert Sebastian

机构信息

Mulliken Center for Theoretical Chemistry, University of Bonn, Bonn, Germany.

出版信息

J Chem Phys. 2021 Sep 14;155(10):104101. doi: 10.1063/5.0061187.

DOI:10.1063/5.0061187
PMID:34525835
Abstract

In this work, we present an altered partition function that leads to an improved calculation of the enthalpy and entropy of vaporization in the framework of quantum cluster equilibrium theory. The changes are based on a previously suggested modification [S. Grimme, Chem. Eur. J. 18, 9955-9964 (2012)] of the molecular entropy calculation in the gas phase. Here, the low energy vibrational frequencies in the vibrational partition function are treated as hindered rotations instead of vibrations. The new scheme is tested on a set of nine organic solvents for the calculation of the enthalpy and entropy of vaporization. The enthalpies and entropies of vaporization show improvements from 6.5 error to 3.3 kJ mol deviation to experiment and from 28.4 error to 13.5 J mol K deviation to experiment, respectively. The effect of the corrected partition function is visible in the different populations of clusters, which become physically more meaningful in that larger clusters are higher populated in the liquid phase and the gas phase is mainly populated by the monomers. Furthermore, the corrected partition function also overcomes technical difficulties and leads to an increased stability of the calculations in regard to the size of the cluster set.

摘要

在这项工作中,我们提出了一种经过改变的配分函数,它能在量子团簇平衡理论框架下改进汽化焓和熵的计算。这些改变基于之前在气相分子熵计算中提出的一种修正方法[S. 格林姆,《化学欧洲杂志》18, 9955 - 9964 (2012)]。在这里,振动配分函数中的低能振动频率被视为受阻转动而非振动。新方案在一组九种有机溶剂上进行了测试,用于计算汽化焓和熵。汽化焓和熵分别从与实验值相差6.5 kJ/mol的误差改进到相差3.3 kJ/mol,以及从与实验值相差28.4 J/(mol·K)的误差改进到相差13.5 J/(mol·K)。修正后的配分函数的影响在不同团簇的分布中可见,这在物理上变得更有意义,因为在液相中较大的团簇占据比例更高,而气相主要由单体占据。此外,修正后的配分函数还克服了技术难题,并在团簇集大小方面提高了计算的稳定性。

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