An Extended Group Additivity Method for Polycyclic Thermochemistry Estimation

作者：Kehang Han, Adeel Jamal, Colin A. Grambow, Zachary J. Buras, William H. Green · 发表于：International Journal of Chemical Kinetics · 年份：2018 · DOI：10.1002/kin.21158 · 被引用次数：45 · 研究领域：Chemical Thermodynamics and Molecular Structure、Computational Drug Discovery Methods、Machine Learning in Materials Science

ABSTRACT Automatic kinetic mechanism generation, virtual high‐throughput screening, and automatic transition state search are currently trending applications requiring exploration of a large molecule space. Large‐scale search requires fast and accurate estimation of molecules' properties of interest, such as thermochemistry. Existing approaches are not satisfactory for large polycyclic molecules: considering the number of molecules being screened, quantum chemistry (even cheap density functional theory methods) can be computationally expensive, and group additivity, though fast, is not sufficiently accurate. This paper provides a fast and moderately accurate alternative by proposing a polycyclic thermochemistry estimation method that extends the group additivity method with two additional algorithms: similarity match and bicyclic decomposition. It significantly reduces H f (298 K) estimation error from over 60 kcal/mol (group additivity method) to around 5 kcal/mol, C p (298 K) error from 9 to 1 cal/mol/K, and S (298 K) error from 70 to 7 cal/mol/K. This method also works well for heteroatomic polycyclics. A web application for estimating thermochemistry by this method is made available at http://rmg.mit.edu/molecule_search .