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From the 1 of 11 linked papers with an AI index.

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20242026
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11 papers

physics.chem-ph2026

Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates

Toshifumi Mori, Kei-ichi Okazaki, Kang Kim +1

The paper presents a framework that uses deep neural networks to learn the committor function for identifying reaction coordinates in complex molecular systems, and applies explain…

cond-mat.soft2026

Machine learning evaluation of structural descriptors for supercooled water

Kohei Yoshikawa, Kokoro Shikata, Kang Kim +1

The anomalous behavior of liquid water is widely associated with a liquid-liquid phase transition between high- and low-density states in the supercooled regime. At the microscopic…

cond-mat.soft2026

Classification of interfacial water governed by water-polymer interactions in hydrated polymers: A molecular dynamics simulation study of ethylene-based and acrylate polymers

Atsuki Hashimoto, Kokoro Shikata, Kang Kim +1

We perform molecular dynamics simulations to investigate hydration structures and dynamics in seven water-containing polymers: PVA, PHEA, PHEMA, PBA, PMEMA, PEG, and PMEA. The anal…

physics.chem-ph2026

IEPDYN: Integral-equation formalism of population dynamics

Kento Kasahara, Ryo Okabe, Chia-en A. Chang +2

We propose the integral-equation formalism of population dynamics (IEPDYN) to describe the population dynamics of distinct configurational states. According to classical reaction d…

physics.chem-ph2026

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…

cond-mat.soft2025

Elucidation of the Correlation between Molecular Conformation and Shear Viscosity of Polymer Melts under Steady-State Shear Flow

Yuhi Sakamaki, Shota Goto, Kang Kim +1

The rheological behavior of polymer melts is strongly influenced by parameters such as chain length, chain stiffness, and architecture. In particular, shear thinning, characterized…