  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 0.053{\AA} 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 the great accuracy and fast inference of React-OT useful
in targeting TSs when exploring chemical reactions with unknown mechanisms.
