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：摘要：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，由于潜在能面的复杂性，精确的 3D 过渡态结构搜索需要大量的计算密集型量子化学计算。本文开发了一个具有物体感知的 SE(3) 等变元扩散模型，满足所有物理对称性和约束条件，用于在单个元素反应中生成一组结构：反应物、过渡态和生成物。给出反应物和生成物，该模型在几秒钟内生成一个过渡态结构，而无需进行量子化学基于优化的花费数小时。生成的过渡态结构与真实的过渡态的平均值达到0.08 Å 的均方根偏差。通过确定不确定性的置信度评估模型，我们在仅对最具挑战性的 14% 反应执行基于量子化学的优化的情况下，实现了所需的反应速率估计精度（2.6 特里）。我们预计该方法在构建有未知机制的大型反应网络方面有用。