巨大反応ネットワークで不斉触媒反応を高精度に予測~機械学習と量子化学を融合し、触媒設計を加速~

2026-04-02 北海道大学

北海道大学の研究チームは、機械学習と量子化学を融合した新手法により、大規模な不斉触媒反応の予測に成功した。200原子を超える複雑な触媒系に対し、ニューラルネットワークポテンシャルと人工力誘起反応法(NNP/AFIR法)を適用し、約2万の安定構造と約4万6千の反応経路からなる巨大な反応ネットワークを構築した。さらに速度論シミュレーション(RCMC法)により、実験で観測される高いエナンチオ選択性を理論的に再現した。本手法は仮定に依存せず触媒機構を解析できる点が特徴であり、医薬品や機能性材料の開発に向けた触媒設計の高度化と効率化に寄与することが期待される。

巨大反応ネットワークで不斉触媒反応を高精度に予測~機械学習と量子化学を融合し、触媒設計を加速~
巨大反応経路ネットワークを用いた不斉触媒反応予測の概念図。

<関連情報>

巨大反応経路ネットワークにおける速度論シミュレーションによるエナンチオ選択性の予測 Predicting Enantioselectivity via Kinetic Simulations on Gigantic Reaction Path Networks

Yu Harabuchi,Ruben Staub,Min Gao,Nobuya Tsuji,Benjamin List,Alexandre Varnek,and Satoshi Maeda
ACS Sentral Science  Published: March 30, 2026
DOI:https://doi.org/10.1021/acscentsci.6c00079

Abstract

Asymmetric catalysts exert control over the reactivity and enantioselectivity of chemical reactions, often through their large size and highly flexible geometries. Most computational approaches rely on a predefined selectivity-determining step and assume that selectivity follows the Boltzmann distribution of transition-state conformers. Going beyond this conventional framework, we constructed a reaction path network that captures the kinetically accessible regions of the potential energy surface. A delta-learning neural network potential (ΔNNP) was constructed and applied to an asymmetric organocatalyst of the imidodiphosphorimidates family, comprising over 200 atoms. The ΔNNP achieved DFT-level accuracy with GFN2-xTB as the baseline. Using the ΔNNP-based single component-artificial force induced reaction (denoted by NNP/AFIR) method, we constructed a reaction path network containing 48,463 paths. Kinetic simulations based on this network predicted the reaction mechanism and enantioselectivity. Within the network, numerous paths, including asynchronous concerted and stepwise mechanisms, were found to be energetically competitive and thus to contribute to the overall kinetics. Traffic volume analysis further revealed that intermediates with negligible yields can still play important kinetic roles. Overall, the NNP/AFIR approach provides a powerful framework not only for deepening mechanistic understanding but also for accelerating the rational design of asymmetric catalytic systems with high reactivity and enantioselectivity.

0500化学一般
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