From the 11 of 228 papers with an AI index.
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- The University of TokyoJP78 papers
- Nagoya UniversityJP47 papers
- Kyushu UniversityJP32 papers
- High Energy Accelerator Research OrganizationJP30 papers
- Tohoku UniversityJP30 papers
- Tokyo Metropolitan UniversityJP30 papers
- Tsinghua UniversityCN29 papers
- Université Paris-SaclayFR28 papers
- University of ChicagoUS28 papers
- University of GenevaCH28 papers
- University of MichiganUS28 papers
- University of WarwickGB28 papers
9 papers · 1 filter
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…
Integral-equation analysis of transient diffusion-limited currents at disk electrodes: Asymptotic expansion and compact approximation
Kazuhiko Seki, Yuko Yokoyama, Masahiro Yamamoto
The transient diffusion-limited current at a disk electrode following a change in interfacial ion concentration induced by a potential step is analyzed with direct relevance to chr…
Fluctuation-induced acceleration of inter-ligand exciton transfer in bis(dipyrrinato)Zn(II) complex
Hiroki Uratani, Hirofumi Sato
Exciton transfer dynamics between chromophores depends on excitonic coupling, which is governed by relative orientation between the chromophores. While the excitonic coupling is tr…
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…
Isotope Effects in 2D correlation infrared Spectra of Water: HEOM Analysis of Molecular Dynamics-Based Machine Learning Models
Kwanghee Park, Ryotaro Hoshino, Yoshitaka Tanimura
We model, simulate, and analyze the intramolecular modes of liquid H2O and D2O to elucidate how energy excitation, relaxation, and vibrational dephasing interplay through anharmoni…
sbml4md: A computational platform for System-Bath Modeling via Molecular Dynamics powered by Machine Learning
Kwanghee Park, Seiji Ueno, Yoshitaka Tanimura
We introduce sbml4md, a newly developed algorithm implemented as a software package to extract parameters of multimode anharmonic Brownian (MAB) models from molecular dynamics (MD)…