Accurate generation of chemical reaction transition states by conditional flow matching

Abstract: Transition state (TS) structures define the critical geometries and energy barriers underlying chemical reactivity, yet their fleeting nature renders them experimentally elusive and drives the reliance on costly, high-throughput density functional theory (DFT) calculations. Here, we introduce TS-GEN, a conditional flow-matching generative model that maps samples from a simple Gaussian prior directly to transition-state saddle-point geometries in a single, deterministic pass. By embedding both reactant and product conformations as conditioning information, TS-GEN learns to transport latent noise to true TS structures via an optimal-transport path, effectively replacing the iterative optimization common in nudged-elastic band or string-method algorithms. TS-GEN delivers unprecedented accuracy, achieving a root-mean-square deviation of $0.004\ \rm{\mathring{A}}$ (vs. $0.103\ \rm{\mathring{A}}$ for prior state-of-the-art) and a mean barrier-height error of $1.019\ {\rm kcal/mol}$ (vs. $2.864\ {\rm kcal/mol}$), while requiring only $0.06\ {\rm s}$ GPU time per inference. Over 87% of generated TSs meet chemical-accuracy criteria ($<1.58\ {\rm kcal/mol}$ error), substantially outpacing existing methods. TS-GEN also exhibits strong transferability to out-of-distribution reactions from a larger database. By uniting sub-angstrom precision, sub-second speed, and broad applicability, TS-GEN will be highly useful for high-throughput exploration of complex reaction networks, paving the way to the exploration of novel chemical reaction mechanisms.