2023-12-15 マサチューセッツ工科大学(MIT)
◆通常、この計算には多くの時間がかかります。この新モデルは、新しい反応や触媒の設計、燃料や薬品などの有用な生成物の開発、または地球上の生命の進化に寄与した可能性のある自然発生の化学反応をモデル化するのに役立つ可能性があります。この手法は、反応が進行するエネルギーの閾値に達すると起こる「遷移状態」を迅速に計算することができ、従来の手法よりもはるかに効率的です。
＜関連情報＞
- https://news.mit.edu/2023/computational-model-captures-elusive-transition-states-1215
- https://www.nature.com/articles/s43588-023-00563-7
オブジェクトを考慮した等変量素反応拡散モデルによる正確な遷移状態生成 Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model
Chenru Duan,Yuanqi Du,Haojun Jia & Heather J. Kulik
Nature Computational Science Published:15 December 2023
DOI:https://doi.org/10.1038/s43588-023-00563-7
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.
