---
res:
bibo_abstract:
- An approach for the construction of vibronically coupled potential energy surfaces
describing reactive collisions is proposed. The scheme utilizes neural networks
to obtain the elements of the diabatic potential energy matrix. The training of
the neural network employs a diabatization by the Ansatz approach and is solely
based on adiabatic electronic energies. Furthermore, no system-specific symmetry
consideration is required. As the first example, the H-2 + Cl -> H + HCl reaction,
which shows a conical intersection in the entrance channel, is studied. The capability
of the approach to accurately reproduce the adiabatic reference energies is investigated.
The accuracy of the fit is found to crucially depend on the number of data points
as well as the size of the neural network. 5000 data points and a neural network
with two hidden layers and 40 neurons in each layer result in a fit with a root
mean square error below 1 meV for the relevant geometries. The coupled diabatic
potential energies are found to vary smoothly with the coordinates, but the conical
intersection is erroneously represented as a very weakly avoided crossing. This
shortcoming can be avoided if symmetry constraints for the coupling potential
are incorporated into the neural network design. Published by AIP Publishing.@eng
bibo_authorlist:
- foaf_Person:
foaf_givenName: Tim
foaf_name: Lenzen, Tim
foaf_surname: Lenzen
- foaf_Person:
foaf_givenName: Uwe
foaf_name: Manthe, Uwe
foaf_surname: Manthe
foaf_workInfoHomepage: http://www.librecat.org/personId=109657
bibo_doi: 10.1063/1.4997995
bibo_issue: '8'
bibo_volume: '147'
dct_date: 2017^xs_gYear
dct_identifier:
- UT:000409143100005
dct_isPartOf:
- http://id.crossref.org/issn/0021-9606
- http://id.crossref.org/issn/1089-7690
dct_language: eng
dct_publisher: Amer Inst Physics@
dct_title: Neural network based coupled diabatic potential energy surfaces for reactive
scattering@
fabio_hasPubmedId: '28863526'
...