Transition state geometry prediction using molecular group contributions.

Area of Science:

• Computational Chemistry
• Chemical Kinetics
• Reaction Mechanism Elucidation

Background:

• Kinetic models for complex chemical systems necessitate numerous reaction rate coefficients, often relying on approximate estimations with uncertain accuracy.
• High-throughput methods for determining rate coefficients using transition state theory (TST) calculations are crucial, but require automated prediction of transition state (TS) geometries.

Purpose of the Study:

• To develop and validate a novel, automated approach for predicting transition state (TS) geometries.
• To enhance the accuracy and efficiency of generating kinetic models for complex chemical systems.

Main Methods:

• A group-additive method was employed to estimate distances between reactive atoms at the TS.
• Three-dimensional TS geometries were constructed using distance geometry and subsequently optimized with electronic structure theory.
• Validation was performed using intrinsic reaction coordinate calculations, establishing a fully automated algorithm.

Main Results:

• The developed algorithm successfully located transition states for 907 out of 1393 hydrogen abstraction reactions in a diisopropyl ketone combustion model over two iterations.
• The group-additive method demonstrated high accuracy in predicting reaction center distances at the TS, achieving root-mean-squared errors of only 0.04 Å with adequate training data.

Conclusions:

• The novel group-additive method provides an accurate and automated means to predict transition state geometries.
• This advancement facilitates the high-throughput determination of reaction rate coefficients, significantly aiding the development of detailed kinetic models for complex chemical systems.