AIを活用し化学反応の理解を自動化する仕組みを開発～深層学習による反応機構の解明をより容易に～

2025-03-14

2025-03-14 九州大学

九州大学先導物質化学研究所の森俊文准教授らの研究チームは、化学反応の理解を自動化するための新たなAI手法を開発しました。従来、化学反応の成否を決定する遷移状態の予測には深層学習が有効とされていましたが、そのモデル構築は煩雑で適用範囲が限られていました。今回、研究チームは深層学習モデルの自動構築に成功し、多数の溶媒分子が存在する環境下でも遷移状態を効率的に予測できることを示しました。さらに、異なるアーキテクチャの深層学習モデルでも、遷移状態の特徴を正確に捉えられることを明らかにしました。この成果は、酵素反応など複雑な化学反応の解明や、AIを用いた化学反応設計、深層学習による物性予測・分子設計理論の深化に寄与することが期待されます。

＜関連情報＞

反応座標に対するディープニューラルネットワークモデルのハイパーパラメータ空間の検討
Investigating the hyperparameter space of deep neural network models for reaction coordinates

Kyohei Kawashima;Takumi Sato;Kei-ichi Okazaki;Kang Kim;Nobuyuki Matubayasi;Toshifumi Mori
APL Machine Learning Published:March 13 2025
DOI:https://doi.org/10.1063/5.0252631

Identifying reaction coordinates (RCs) is a key to understanding the mechanism of reactions in complex systems. Deep neural network (DNN) and machine learning approaches have become a powerful tool to find the RC. On the other hand, the hyperparameters that determine the DNN model structure can be highly flexible and are often selected intuitively and in a non-trivial and tedious manner. Furthermore, how the hyperparameter choice affects the RC quality remains obscure. Here, we explore the hyperparameter space by developing the hyperparameter tuning approach for the DNN model for RC and investigate how the parameter set affects the RC quality. The DNN model is built to predict the committor along the RC from various collective variables by minimizing the cross-entropy function; the hyperparameters are automatically determined using the Bayesian optimization method. The approach is applied to study the isomerization of alanine dipeptide in vacuum and in water, and the features that characterize the RC are extracted using the explainable AI (XAI) tools. The results show that the DNN models with diverse structures can describe the RC with similar accuracy, and furthermore, the features analyzed by XAI are highly similar. This indicates that the hyperparameter space is multimodal. The electrostatic potential from the solvent to the hydrogen H₁₈ plays an important role in the RC in water. The current study shows that the structure of the DNN models can be rather flexible, while the suitably optimized models share the same features; therefore, a common mechanism from the RC can be extracted.