61st Annual Report on Research 2016

We have developed an automated algorithm that predicts reaction transition state geometries, interacts with readily available computational chemistry software to optimize and validate the transition state, then interfaces with an open-source software package to perform transition state theory calculations, to determine reaction rate expressions. We have tested this overall workflow, determined when it can perform better than previous estimation methods, and found the dominant sources of error.

Motivation

Detailed kinetic modeling of combustion has made great progress in recent decades, with models now able to predict and explain complex combustion phenomena for a range of fuels at varied conditions. These models contain many thousands of reaction rate expressions, the vast majority of which are currently estimated. Transition State Theory (TST), coupled with modern computational quantum chemistry methods, would allow such reaction rates to be calculated with high accuracy. As these methods improve and high-performance computers get more powerful, the logical progression is to calculate ab initio all the reaction rates that are currently estimated, or at least the ones to which the model predictions are sensitive. The bottleneck is the human input currently required to guess the geometry of the transition state (TS) – the positions of the atoms at the midpoint of the chemical reaction – required to start a TS calculation. This project aims to predict these TS geometries algorithmically, so that the entire TST calculation can be performed automatically, allowing high-throughput calculation of these important reaction rates.

Progress

During a reaction, most of the reacting molecule closely resembles the geometry of the reactant, which can be predicted using existing distance geometry techniques; the unknown segment of the geometry at the transition state (TS) is at the reaction center, where bonds are being broken or formed. By predicting the distances between a handful of atoms at the reaction center, we are thus able to predict the geometry of the entire TS. We developed a group contribution method to predict the interatomic distances at the reaction center, based on the molecular functional groups reacting. The values for a group are calculated by linear least squares regression on a training set of distances from optimized and validated transition states. The values are organized in a hierarchical tree database, with the top nodes representing the most general template for the reaction. As the tree is descended, the functional groups become more specific, with the most specific groups residing at the base of the tree. If a specific group has not been trained, the group estimation will use the parent group, climbing the tree until a value is found. With a properly designed group tree structure, this allows good estimation of transition states even when training data are sparse.

Using the interatomic distances predicted by our group additive scheme, with distance-geometry methods in the open-source chemoinformatics toolkit RDKit, and constrained optimization with force fields and density functional theory, we have developed an algorithm to create 3D geometry estimates that can be used to start TS optimization searches with quantum chemistry packages.

Once optimized to a saddle point on the potential energy landscape, an intrinsic reaction coordinate (IRC) calculation is performed to verify that the transition state connects the expected reactants and products. This completes the fully automated pipeline that estimates, optimizes, and validates transition states. The optimized and validated transition states are then added to the training data and used to improve the group additive predictions.

In the second year we have improved the algorithm for determining geometries, increasing the success rate to 70%, and have extended the method to two additional reaction families such that it can now find transition states for Hydrogen Abstraction, Intramolecular Hydrogen Migration, and Beta Scission reactions. We have also coupled the algorithm to a code for determining Symmetry Number, and another for performing Canonical Transition State Theory calculations, so that the overall “AutoTST” algorithm can perform an entire reaction rate calculation without human input. Testing this on over 1000 reactions from a model for combustion of Butanol, we find the algorithm performs well. Careful benchmark calculations of a selection of reactions for which the AutoTST method disagreed with the published model reveals that (1) the AutoTST results are usually better than estimates that were made by a poor analogy (which happens frequently because of the scarcity of kinetic data) and (2) the main sources of error in the AutoTST results are neglecting hindered rotors (using the harmonic oscillator approximation) and determining the symmetry number incorrectly.

Impacts

This project will enable high-throughput TST calculations to provide improved reaction kinetics for automatically generated detailed kinetic models of combustion and fuel processing. Coupled with a separate NSF-funded project to interpret and resolve discrepancies in published kinetic models, the project will quickly impact the combustion modeling community. It will also facilitate several other research projects being undertaken in our research group at Northeastern: predicting the effects of solvents on reaction kinetics, developing reaction rate rules for new reaction families in RMG, and adding reaction kinetics for species containing new elements such as silicon and chlorine.