Reaction-Transformation-Aware Flow Matching for Generalizable Transition State Generation
Transition-state (TS) structures define the energetic barriers and mechanistic pathways of elementary chemical reactions, yet their identification remains computationally demanding because conventional saddle-point searches require expensive quantum-mechanical calculations. Recent machine-learning approaches have accelerated TS generation by predicting structures from reaction endpoint information, but they primarily learn geometric correspondence between endpoints and TSs, leaving the structural transformations underlying elementary reactions implicitly represented. To address this limitation, we introduce TransTS, a reaction-transformation-aware framework for generalizable TS generation...
| Authors | Kaipeng Zeng, Wenxi Zhai, Shengrui Xu et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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