Conformational Sampling for Transition State Searches on a Computational Budget

Transition state searches are the basis for characterizing reaction mechanisms and activation energies, and are thus central to myriad chemical applications. Nevertheless, common search algorithms are sensitive to molecular conformation and the conformational space of even medium-sized reacting systems are too complex to explore with brute force. Here we show that it is possible to train a classifier to learn the features of conformers that conduce successful transition state searches, such that optimal conformers can be down-selected before incurring the cost of a high-level transition state search. To this end, we have benchmarked the use of a modern conformational generation algorithm with our reaction prediction methodology, Yet Another Reaction Program (YARP), for reaction prediction tasks. We demonstrate that neglecting conformer contributions leads to qualitatively incorrect activation energy estimations for a broad range of reactions, whereas a simple random forest classifier can be used to reliably down-select low-barrier conformers. We also compare the relative advantage of performing conformational sampling on reactant, product, and putative transition state geometries. The robust performance of this relatively simple machine learning classifier mitigates cost as a factor when implementing conformational sampling into contemporary reaction prediction workflows.