No Access Submitted: 24 October 2018 Accepted: 19 March 2019 Published Online: 03 April 2019
J. Chem. Phys. 150, 134103 (2019); https://doi.org/10.1063/1.5078394
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.
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  1. © 2019 Author(s). Published under license by AIP Publishing.