React-OT: Optimal Transport for Generating Transition State in Chemical Reactions
This work addresses the high computational cost of transition state searches for chemists and researchers, enabling faster exploration of reaction networks, though it is incremental as it builds on existing optimization methods.
The paper tackled the challenge of computationally generating transition state structures in chemical reactions by developing React-OT, an optimal transport approach that produces highly accurate structures with a median RMSD of 0.053Å and barrier height error of 1.06 kcal/mol in 0.4 seconds per reaction.
Transition states (TSs) are transient structures that are key in understanding reaction mechanisms and designing catalysts but challenging to be captured in experiments. Alternatively, many optimization algorithms have been developed to search for TSs computationally. Yet the cost of these algorithms driven by quantum chemistry methods (usually density functional theory) is still high, posing challenges for their applications in building large reaction networks for reaction exploration. Here we developed React-OT, an optimal transport approach for generating unique TS structures from reactants and products. React-OT generates highly accurate TS structures with a median structural root mean square deviation (RMSD) of 0.053Å and median barrier height error of 1.06 kcal/mol requiring only 0.4 second per reaction. The RMSD and barrier height error is further improved by roughly 25\% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB. We envision that the remarkable accuracy and rapid inference of React-OT will be highly useful when integrated with the current high-throughput TS search workflow. This integration will facilitate the exploration of chemical reactions with unknown mechanisms.