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• W2934857086 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." @default.
• W2934857086 created "2019-04-11" @default.
• W2934857086 creator A5009585241 @default.
• W2934857086 date "2019-04-03" @default.
• W2934857086 modified "2023-10-18" @default.
• W2934857086 title "Data sampling scheme for reproducing energies along reaction coordinates in high-dimensional neural network potentials" @default.
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• W2934857086 doi "https://doi.org/10.1063/1.5078394" @default.
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