5 citations · 10 across the 4 of their papers we have counts for
4 papers
Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates
Toshifumi Mori, Kei-ichi Okazaki, Kang Kim +1
In complex molecular systems, the reaction coordinate (RC) that characterizes transition pathways is essential to understand underlying molecular mechanisms. This review surveys a…
Deep learning of committor for ion dissociation and interpretable analysis of solvent effects using atom-centered symmetry functions
Kenji Okada, Kazushi Okada, Kei-ichi Okazaki +3
The association and dissociation of ion pairs in water are fundamental to physical chemistry, yet their reaction coordinates are complex, involving not only interionic distance but…
Investigating the hyperparameter space of deep neural network models for reaction coordinates
Kyohei Kawashima, Takumi Sato, Kei-ichi Okazaki +3
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 becom…
Unveiling interatomic distances influencing the reaction coordinates in alanine dipeptide isomerization: An explainable deep learning approach
Kazushi Okada, Takuma Kikutsuji, Kei-ichi Okazaki +3
The present work shows that the free energy landscape associated with alanine dipeptide isomerization can be effectively represented by specific interatomic distances without expli…