Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation-Reference-Cited by-同舟云学术

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

Published:2018-05-07 Issue:11 Volume:9 Page:2725-2732
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Hu Deping¹²^ORCID,Xie Yu¹^ORCID,Li Xusong¹²^ORCID,Li Lingyue²^ORCID,Lan Zhenggang¹²

Affiliation:

1. CAS Key Laboratory of Biobased Materials, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China

2. University of Chinese Academy of Sciences, Beijing 100049, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.8b00684

Reference92 articles.

1. Conical Intersections

2. Conical Intersections

3. Ultrafast Excited-State Dynamics in Nucleic Acids

4. DNA Excited-State Dynamics: From Single Bases to the Double Helix

5. Nonadiabatic Events and Conical Intersections

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