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  Deep Learning for Nonadiabatic Excited-State Dynamics

Chen, W.-K., Liu, X.-Y., Fang, W.-H., Dral, P. O., & Cui, G. (2018). Deep Learning for Nonadiabatic Excited-State Dynamics. The Journal of Physical Chemistry Letters, 9(23), 6702-6708. doi:10.1021/acs.jpclett.8b03026.

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 Creators:
Chen, Wen-Kai1, Author
Liu, Xiang-Yang1, Author
Fang, Wei-Hai1, Author
Dral, Pavlo O.2, Author              
Cui, Ganglong1, Author
Affiliations:
1Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China, ou_persistent22              
2Research Department Thiel, Max-Planck-Institut für Kohlenforschung, Max Planck Society, ou_1445590              

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 Abstract: In this work we show that deep learning (DL) can be used for exploring complex and highly nonlinear multistate potential energy surfaces of polyatomic molecules and related nonadiabatic dynamics. Our DL is based on deep neural networks (DNNs), which are used as accurate representations of the CASSCF ground- and excited-state potential energy surfaces (PESs) of CH2NH. After geometries near conical intersection are included in the training set, the DNN models accurately reproduce excited-state topological structures; photoisomerization paths; and, importantly, conical intersections. We have also demonstrated that the results from nonadiabatic dynamics run with the DNN models are very close to those from the dynamics run with the pure ab initio method. The present work should encourage further studies of using machine learning methods to explore excited-state potential energy surfaces and nonadiabatic dynamics of polyatomic molecules.

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Language(s): eng - English
 Dates: 2018-10-012018-11-072018-11-072018-12-06
 Publication Status: Published in print
 Pages: 7
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1021/acs.jpclett.8b03026
 Degree: -

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Title: The Journal of Physical Chemistry Letters
  Abbreviation : J. Phys. Chem. Lett.
Source Genre: Journal
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Publ. Info: Washington, DC : American Chemical Society
Pages: - Volume / Issue: 9 (23) Sequence Number: - Start / End Page: 6702 - 6708 Identifier: CoNE: https://pure.mpg.de/cone/journals/resource/1948-7185