Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model
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2304.06174.pdf
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12.16 MB
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4c54886aab9666658b231f78e623f1da
Author(s)
Duan, Chenru
Du, Yuanqi
Jia, Haojun
Kulik, Heather J.
Date Issued
December 15, 2023
Journal
Nature Computational Science
Publisher
Nature
Citation
Duan, Chenru, Du, Yuanqi, Jia, Haojun and Kulik, Heather J. 2023. "Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model." Nature Computational Science.
Version
Author's final manuscript
Abstract
Transition state search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D transition state 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, transition state and product—in an elementary reaction. Provided reactant and product, this model generates a transition state structure in seconds instead of hours, which is typically required when performing quantum-chemistry-based optimizations. The generated transition state structures achieve a median of 0.08 Å root mean square deviation compared to the true transition state. With a confidence scoring model for uncertainty quantification, we approach an accuracy required for reaction barrier estimation (2.6 kcal mol–1) by only performing quantum chemistry-based optimizations on 14% of the most challenging reactions. We envision usefulness for our approach in constructing large reaction networks with unknown mechanisms.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Department of Chemical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1038/s43588-023-00563-7
