Applied Chemicals and Materials Division, National Institute of Standards and Technology, Boulder, Colorado 80305, United States.
Institute of Power Engineering, Faculty of Mechanical Science and Engineering, Technische Universität Dresden, Helmholtzstraße 14, 01069 Dresden, Germany.
J Chem Theory Comput. 2020 Apr 14;16(4):2635-2646. doi: 10.1021/acs.jctc.9b01016. Epub 2020 Mar 6.
The COSMO-SAC modeling approach has found wide application in science as well as in a range of industries due to its good predictive capabilities. While other models for liquid phases, as for example UNIFAC, are in general more accurate than COSMO-SAC, these models typically contain many adjustable parameters and can be limited in their applicability. In contrast, the COSMO-SAC model only contains a few universal parameters and subdivides the molecular surface area into charged segments that interact with each other. In recent years, additional improvements to the construction of the sigma profiles and evaluation of activity coefficients have been made. In this work, we present a comprehensive description of how to postprocess the results of a COSMO calculation through to the evaluation of thermodynamic properties. We also assembled a large database of COSMO files, consisting of 2261 compounds, freely available to academic and noncommercial users. We especially focus on the documentation of the implementation and provide the optimized source code in C++, wrappers in Python, and sample sigma profiles calculated from each approach, as well as tests and validation results. The misunderstandings in the literature relating to COSMO-SAC are described and corrected. The computational efficiency of the implementation is demonstrated.
COSMO-SAC 建模方法由于其良好的预测能力，在科学和一系列工业中得到了广泛的应用。虽然其他用于液相的模型，例如 UNIFAC，通常比 COSMO-SAC 更准确，但这些模型通常包含许多可调节的参数，并且其适用性可能受到限制。相比之下，COSMO-SAC 模型仅包含少数通用参数，并将分子表面积细分为相互作用的带电段。近年来，对 sigma 轮廓的构建和活度系数的评估进行了额外的改进。在这项工作中，我们全面介绍了如何通过 COSMO 计算结果的后处理来评估热力学性质。我们还组装了一个包含 2261 种化合物的 COSMO 文件大型数据库，可供学术和非商业用户免费使用。我们特别关注实现的文档，并提供 C++中的优化源代码、Python 中的包装器以及从每种方法计算的示例 sigma 轮廓，以及测试和验证结果。描述并纠正了文献中与 COSMO-SAC 相关的误解。演示了实现的计算效率。
