Transition state geometry prediction using molecular group contributions

Detailed kinetic models to aid the understanding of complex chemical systems require many thousands of reaction rate coefficients, most of which are estimated, some quite approximately and with unknown uncertainties. This motivates the development of high-throughput methods to determine rate coefficients via transition state theory calculations, which requires the automatic prediction of transition state (TS) geometries. We demonstrate a novel approach to predict TS geometries using a group-additive method. Distances between reactive atoms at the TS are estimated using molecular group values, with the 3D geometry of the TS being constructed by distance geometry. The estimate is then optimized using electronic structure theory and validated using intrinsic reaction coordinate calculations, completing the fully automatic algorithm to locate TS geometries. The methods were tested using a diisopropyl ketone combustion model containing 1393 hydrogen abstraction reactions, of which transition states were found for 907 over two iterations of the algorithm. With sufficient training data, molecular group contributions were shown to successfully predict the reaction center distances of transition states with root-mean-squared errors of only 0.04 Å.