Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Lichtenbergstr. 4, 85747 Garching, Germany.
SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, Stanford, California 94305, USA.
J Chem Phys. 2019 Jan 28;150(4):041710. doi: 10.1063/1.5050938.
In computer simulations of solvation effects on chemical reactions, continuum modeling techniques regain popularity as a way to efficiently circumvent an otherwise costly sampling of solvent degrees of freedom. As effective techniques, such implicit solvation models always depend on a number of parameters that need to be determined earlier. In the past, the focus lay mostly on an accurate parametrization of water models. Yet, non-aqueous solvents have recently attracted increasing attention, in particular, for the design of battery materials. To this end, we present a systematic parametrization protocol for the Self-Consistent Continuum Solvation (SCCS) model resulting in optimized parameters for 67 non-aqueous solvents. Our parametrization is based on a collection of ≈6000 experimentally measured partition coefficients, which we collected in the Solv@TUM database presented here. The accuracy of our optimized SCCS model is comparable to the well-known universal continuum solvation model (SMx) family of methods, while relying on only a single fit parameter and thereby largely reducing statistical noise. Furthermore, slightly modifying the non-electrostatic terms of the model, we present the SCCS-P solvation model as a more accurate alternative, in particular, for aromatic solutes. Finally, we show that SCCS parameters can, to a good degree of accuracy, also be predicted for solvents outside the database using merely the dielectric bulk permittivity of the solvent of choice.
在化学反应的溶剂效应的计算机模拟中，连续体建模技术作为一种有效地规避溶剂自由度采样成本的方法重新受到关注。作为有效的技术，这种隐式溶剂模型总是依赖于需要提前确定的许多参数。过去，重点主要放在水模型的精确参数化上。然而，非水溶剂最近引起了越来越多的关注，特别是在电池材料的设计方面。为此，我们提出了一种用于自洽连续体溶剂化（SCCS）模型的系统参数化方案，为 67 种非水溶剂生成了优化的参数。我们的参数化基于大约 6000 个实验测量的分配系数的集合，这些系数是在我们在此处介绍的 Solv@TUM 数据库中收集的。我们优化的 SCCS 模型的准确性可与著名的通用连续体溶剂化模型（SMx）家族方法相媲美，而仅依赖于单个拟合参数，从而大大降低了统计噪声。此外，通过稍微修改模型的非静电项，我们提出了 SCCS-P 溶剂化模型作为一种更准确的替代方法，特别是对于芳香溶质。最后，我们表明，仅使用所选溶剂的介电常数，SCCS 参数也可以在很大程度上准确地预测数据库之外的溶剂。
