Reconstruction Is Not Transportability: Transition-State-Aware Tokenization for Reaction Pathway Flow Matching

Mapping the pathway that carries reactants through the transition state to products is a bottleneck in mechanistic analysis and reaction screening, and the fastest learned methods now produce a complete pathway in one pass from the endpoint geometries alone. Generating it in a learned latent space separates geometric encoding from transport, but a latent trained only to reconstruct is not the one a flow transports best. We call this the representation–transport mismatch: a pathway is recovered from its latent to 0.052 Å while that latent stays hard for the flow to predict. We propose REACFLOW, a two-stage framework that shapes the pathway latent for transport rather than for reconstruction alone. Its autoencoder is pretrained with a climbing-image NEB force-response head, which keeps search-relevant motion readable after compression, and an interval-completion objective, whose extreme case is the endpoint interpolation the generator starts from. We then freeze it and train a conditional flow-matching model to carry the interpolation latent to the target latent; generation uses four velocity-field evaluations followed by the frozen decoder, with no energy or force evaluations. We test REACFLOW on two benchmarks, Transition1x and Halo8, and it attains the lowest geometry error of any method compared on both (0.215/0.175 Å TS-RMSD and 0.155/0.165 Å IRC-RMSD), improving generation by 24.6–36.7% over reconstruction-only pretraining while clean reconstruction stays flat at 0.052–0.056 Å.