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Yasuharu Okamoto - One of the best experts on this subject based on the ideXlab platform.

  • data sampling scheme for reproducing energies along reaction coordinates in high dimensional neural network potentials
    Journal of Chemical Physics, 2019
    Co-Authors: Yasuharu Okamoto
    Abstract:

    We propose a data sampling scheme for high-dimensional neural network potentials that can predict energies along the reaction pathway calculated using the hybrid density functional theory. We observed that a data sampling scheme that combined partial geometry optimization of intermediate structures with random displacement of atoms successfully predicted the energies along the reaction path with respect to five chemical reactions: Claisen Rearrangement, Diels–Alder reaction, [1,5]-sigmatropic hydrogen shift, concerted hydrogen transfer in the water hexamer, and Cornforth Rearrangement.We propose a data sampling scheme for high-dimensional neural network potentials that can predict energies along the reaction pathway calculated using the hybrid density functional theory. We observed that a data sampling scheme that combined partial geometry optimization of intermediate structures with random displacement of atoms successfully predicted the energies along the reaction path with respect to five chemical reactions: Claisen Rearrangement, Diels–Alder reaction, [1,5]-sigmatropic hydrogen shift, concerted hydrogen transfer in the water hexamer, and Cornforth Rearrangement.