Y
Yu Harabuchi
Researcher at Hokkaido University
Publications - 75
Citations - 1540
Yu Harabuchi is an academic researcher from Hokkaido University. The author has contributed to research in topics: Chemistry & Conical intersection. The author has an hindex of 17, co-authored 59 publications receiving 1053 citations. Previous affiliations of Yu Harabuchi include Reaction Design & National Presto Industries.
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Orbital Energy-Based Reaction Analysis of S N 2 Reactions
TL;DR: Comparing the calculated results of the SN2 reactions in gas phase and in aqueous solution shows that the contributing orbitals significantly depend on solvent effects and these orbitals can be correctly determined by this theory.
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On-the-fly molecular dynamics study of the excited-state branching reaction of α-methyl-cis-stilbene
TL;DR: In this paper, the branching reaction of α-methyl-cis-stilbene into its trans-mSB and 4a,4b-dihydrophenanthrene (DHP) forms upon ππ∗ excitation was examined theoretically by exploring the excited-state potential energy surface and using on-the-fly molecular dynamics simulations at the spin-flip time-dependent density functional theory (SF-TDDFT) level of theory.
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Trajectory on-the-fly molecular dynamics approach to tunneling splitting in the electronic excited state: A case of tropolone.
TL;DR: The semiclassical tunneling method is applied to evaluate the tunneling splitting of tropolone due to the intramolecular proton transfer in the electronic excited state, first time, in a framework of the trajectory on‐the‐fly molecular dynamics (TOF‐MD) approach and it is shown that the tunneled splitting decreases as the bath‐mode energy increases.
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Exploring potential crossing seams in periodic systems: Intersystem crossing pathways in the benzene crystal.
TL;DR: The intersystem crossing (ISC) pathways of triplet benzene molecules in a benzene crystal were investigated theoretically and the first reported use of a GP/SC-AFIR calculation using a density functional theory calculation with periodic boundary conditions was reported.
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Selecting molecules with diverse structures and properties by maximizing submodular functions of descriptors learned with graph neural networks
TL;DR: SubMo-GNN as mentioned in this paper proposes a new approach for selecting a subset of diverse molecules from a given molecular list by using two existing techniques studied in machine learning and mathematical optimization: graph neural networks (GNNs) for learning vector representation of molecules and a diverse selection framework called submodular function maximization.