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Finding molecular transition states has long been hamstrung by the extreme computational cost of ab initio saddle-point searches and inference-time guidance in geometric diffusion models. The authors address this by building Tr猫fle, a one-step generative flow model that directly amortizes transition-state physical constraints into network parameters via reinforcement learning using an efficient surrogate reward. Tr猫fle delivers a 30脳 throughput speedup over multi-step baselines, nearly doubles per-attempt recovery on unseen bimolecular reaction channels from 28% to 50%, and generalizes zero-shot to much larger molecular systems.
Amortizing quantum physical constraints into a one-step flow model via surrogate RL nearly doubles transition-state recovery on unseen reactions while running 30脳 faster than guided diffusion baselines.
Transition-state searches remain a major bottleneck in reaction discovery, as identifying valid saddle-point structures requires numerous expensive quantum-chemical calculations. Generative models can reduce this burden by proposing candidates from reactant and product geometries, but supervised training on geometries alone does not enforce the physical conditions required of a transition state, while enforcing them during sampling is costly. We introduce Tr\`efle, a one-step flow model that amortizes physics-guided generation into training: a mean-flow objective replaces multi-step sampling, a low-cost surrogate replaces density functional theory (DFT) reward evaluations, and reinforcement-learning post-training with rewards that enforce those conditions replaces inference-time guidance. Tr\`efle generates accurate transition states at $30\times$ the throughput of multi-step baselines. It transfers few-shot to ten unseen transition metals and generalizes zero-shot to molecules far larger than any in training. With DFT refinement and reaction-path validation, Tr\`efle-seeded searches recover all 13 unseen $\gamma$-ketohydroperoxide channels, while post-training raises per-attempt recovery from 28% to 50% across 85 unseen bimolecular channels. Tr\`efle thus provides fast, reliable seeds for automated reaction discovery.
