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基于密度泛函理论对隐式溶剂化模型中溶液熵的预测

On the prediction by density functional theory of entropies in solution within implicit solvation models.

作者信息

Castor-Villegas Victoria, Tognetti Vincent, Joubert Laurent

机构信息

Normandy Univ., COBRA UMR 6014 & FR 3038, Université de Rouen, INSA Rouen, CNRS, 1 rue Tesnière, 76821, Mont St Aignan Cedex, France.

出版信息

J Mol Model. 2024 Dec 4;31(1):7. doi: 10.1007/s00894-024-06225-3.

Abstract

CONTEXT

Entropies are fundamental contributions to Gibbs energies that carry important chemical information, in particular when investigating reaction mechanisms. However, evaluating them in solution is far from being straightforward. In this paper, we focus on its evaluation within the framework of implicit solvation models. To this aim, successive corrections (with increased complexity) involving only contributions available from any standard quantum chemistry code and macroscopic solvent properties are built and assessed by comparison to more than one hundred experimental entropy values measured in a liquid phase. It turns out that significant improvement with respect to the standard ideal gas approximation can be achieved at an almost negligible computational cost, affording a robust and transferable predictive model.

METHODS

DFT calculations with the ADF software at the PBE or PBE0/TZ2P level of theory with COSMO solvent model. Python scripts for regressions.

摘要

背景

熵是对吉布斯自由能的基本贡献,承载着重要的化学信息,尤其是在研究反应机理时。然而,在溶液中评估熵并非易事。在本文中,我们专注于在隐式溶剂化模型框架内对其进行评估。为此,构建了仅涉及任何标准量子化学代码可得贡献和宏观溶剂性质的连续校正(复杂度不断增加),并通过与液相中测量的一百多个实验熵值进行比较来评估。结果表明,相对于标准理想气体近似可以在几乎可忽略的计算成本下实现显著改进,从而提供一个稳健且可转移的预测模型。

方法

使用ADF软件在PBE或PBE0/TZ2P理论水平以及COSMO溶剂模型下进行密度泛函理论计算。用于回归的Python脚本。

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