Optimal transport for generating transition states in chemical reactions
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s42256-025-01010-0.pdf
Description
Published version
Size
9.15 MB
Format
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Checksum (MD5)
04e5d2a82c4ab421df0add5e5b691a6e
Author(s)
Duan, Chenru
Liu, Guan-Horng
Du, Yuanqi
Chen, Tianrong
Zhao, Qiyuan
Jia, Haojun
Gomes, Carla P
Theodorou, Evangelos A
Kulik, Heather J
Date Issued
April 23, 2025
Journal
Nature Machine Intelligence
Publisher
Springer Science and Business Media LLC
Citation
Duan, C., Liu, GH., Du, Y. et al. Optimal transport for generating transition states in chemical reactions. Nat Mach Intell 7, 615–626 (2025).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Department of Chemical Engineering
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DOI of Published Version
https://doi.org/10.1038/s42256-025-01010-0
