Transition state (TS) search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D TS structures, however, requires numerous computationally intensive quantum chemistry calculations due to the complexity of potential energy surfaces. Here, we developed an object-aware SE(3) equivariant diffusion model that satisfies all physical symmetries and constraints for generating sets of structures - reactant, TS, and product - in an elementary reaction. Provided reactant and product, this model generates a TS structure in seconds instead of hours required when performing quantum chemistry-based optimizations. The generated TS structures achieve a median of 0.08 {\AA} root mean square deviation compared to the true TS. With a confidence scoring model for uncertainty quantification, we approach an accuracy required for reaction rate estimation (2.6 kcal/mol) by only performing quantum chemistry-based optimizations on 14\% of the most challenging reactions. We envision the proposed approach useful in constructing large reaction networks with unknown mechanisms.

翻译：过渡态搜索是化学中阐明反应机理和探索反应网络的关键。然而，由于势能面的复杂性，精确三维过渡态结构的搜索需要大量高计算成本的量子化学计算。为此，我们开发了一种面向目标的SE(3)等变扩散模型，该模型满足所有物理对称性和约束条件，用于生成基元反应中的结构集合——反应物、过渡态和产物。在给定反应物和产物的条件下，该模型可在数秒内生成过渡态结构，而基于量子化学的优化方法需要数小时。生成的过渡态结构与真实值相比，均方根偏差中位数达到0.08埃。基于不确定性量化的置信度评分模型，我们仅对14%最具挑战性的反应进行量子化学优化，即可达到反应速率估算所需的精度（2.6千卡/摩尔）。我们设想该方法可用于构建机制未知的大规模反应网络。