Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model
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 surfa...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Nature
2023
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| Online Access: | https://hdl.handle.net/1721.1/153174 |
| _version_ | 1811080529986977792 |
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| author | Duan, Chenru Du, Yuanqi Jia, Haojun Kulik, Heather J. |
| author2 | Massachusetts Institute of Technology. Department of Chemistry |
| author_facet | Massachusetts Institute of Technology. Department of Chemistry Duan, Chenru Du, Yuanqi Jia, Haojun Kulik, Heather J. |
| author_sort | Duan, Chenru |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:33:11Z |
| format | Article |
| id | mit-1721.1/153174 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:33:11Z |
| publishDate | 2023 |
| publisher | Nature |
| record_format | dspace |
| spelling | mit-1721.1/1531742023-12-16T03:38:49Z Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model Duan, Chenru Du, Yuanqi Jia, Haojun Kulik, Heather J. Massachusetts Institute of Technology. Department of Chemistry Massachusetts Institute of Technology. Department of Chemical Engineering 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. 2023-12-15T14:46:38Z 2023-12-15T14:46:38Z 2023-12-15 Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/153174 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. en_US https://doi.org/10.1038/s43588-023-00563-7 Nature Computational Science Creative Commons Attribution-Noncommercial-Share Alike An error occurred on the license name. https://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Nature MIT News office |
| spellingShingle | Duan, Chenru Du, Yuanqi Jia, Haojun Kulik, Heather J. Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model |
| title | Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model |
| title_full | Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model |
| title_fullStr | Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model |
| title_full_unstemmed | Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model |
| title_short | Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model |
| title_sort | accurate transition state generation with an object aware equivariant elementary reaction diffusion model |
| url | https://hdl.handle.net/1721.1/153174 |
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