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, Toshifumi Mori, Kang Kim, Nobuyuki Matubayasi

TL;DR
This paper uses deep learning with atom-centered symmetry functions to identify and interpret reaction coordinates for ion pair dissociation in water, providing insights into solvent effects and transition pathways.
Contribution
It introduces a neural network approach employing ACSFs to accurately determine reaction coordinates and uses explainable AI to interpret solvent contributions in ion dissociation.
Findings
Deep learning effectively captures transition pathways.
ACSF descriptors reveal solvent's role in ion dissociation.
Explainable AI identifies key solvent features influencing reactions.
Abstract
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 also solvent-mediated hydration structures. These processes are often represented by free-energy landscapes constructed from collective variables (CVs), such as interionic distance and water bridging structures; however, it remains uncertain whether such representations reliably capture the transition pathways between the two associated and dissociated states. In this study, we employ deep learning to identify reaction coordinates for NaCl ion pair association and dissociation in water, using the committor as a quantitative measure of progress along the transition pathway through the transition state. The solvent environment surrounding the ions is encoded through descriptors based on atom-centered symmetry…
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