2025-04-23 マサチューセッツ工科大学 (MIT)
＜関連情報＞
- https://news.mit.edu/2025/new-model-predicts-chemical-reactions-no-return-point-0423
- https://www.nature.com/articles/s42256-025-01010-0
- https://tiisys.com/blog/2023/12/19/post-130858/
化学反応における遷移状態生成のための最適輸送法 Optimal transport for generating transition states in chemical reactions
Chenru Duan,Guan-Horng Liu,Yuanqi Du,Tianrong Chen,Qiyuan Zhao,Haojun Jia,Carla P. Gomes,Evangelos A. Theodorou & Heather J. Kulik
Nature Machine Intelligence Published:23 April 2025
Abstract
Transition states (TSs) are transient structures that are key to understanding reaction mechanisms and designing catalysts but challenging to capture in experiments. Many optimization algorithms have been developed to search for TSs computationally. Yet, the cost of these algorithms driven by quantum chemistry methods (usually density functional theory) is still high, posing challenges for their applications in building large reaction networks for reaction exploration. Here we developed React-OT, an optimal transport approach for generating unique TS structures from reactants and products. React-OT generates highly accurate TS structures with a median structural root mean square deviation of 0.053 Å and median barrier height error of 1.06 kcal mol−1 requiring only 0.4 s per reaction. The root mean square deviation and barrier height error are further improved by roughly 25% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB. We envision that the remarkable accuracy and rapid inference of React-OT will be highly useful when integrated with the current high-throughput TS search workflow. This integration will facilitate the exploration of chemical reactions with unknown mechanisms.
