@Article{JAMS-9-2,
author = {},
title = {An Potential Energy Surface for the $O+O_2$ Reaction Using Neural Network Approach},
journal = {Journal of Atomic and Molecular Sciences},
year = {2018},
volume = {9},
number = {2},
pages = {25--27},
abstract = {<p style="text-align: justify;">A new potential energy surface (PES) of the $O_3$ system was reported, based on the PESs reported
by Dawes et al. and Schinke et al. The PES of $O_3$&nbsp;reported by Dawes et al. was fitted using least square
method based upon accurate high level ab initio points, and has been applied to many dynamics calculations.
However, it is computationally slow and need lots of time to obtain the energy points from the PES. At the
same time, the threshold of the ab initio points is low as 2.6 eV, relative to the minimum of the PES, which
limits its application. On the contrary, the PES reported by Schinke et al. numerically is fast and extends to
high energy. In this work, we first calculated the energy points from these two PESs. Then we fitted these
energy points using PIP-NN approach. In this way, a revised version of the PES of $O_3$&nbsp;was constructed and we
used it to perform quantum reactive scattering dynamics calculation.</p>},
issn = {2079-7346},
doi = {https://doi.org/10.4208/jams.101518.112818a},
url = {https://global-sci.com/article/74189/an-potential-energy-surface-for-the-oo-2-reaction-using-neural-network-approach}
}