Nonadiabatic Derivative Couplings Calculated Using Information of Potential Energy Surfaces without Wavefunctions: Ab Initio and Machine Learning Implementations.

Chen, Wen-Kai; Wang, Sheng-Rui; Liu, Xiang-Yang; Fang, Wei-Hai; Cui, Ganglong

Chen, Wen-Kai; Wang, Sheng-Rui; Liu, Xiang-Yang; Fang, Wei-Hai; Cui, Ganglong.

Afiliación

Chen WK; Hebei Key Laboratory of Inorganic Nano-Materials, College of Chemistry and Materials Science, Hebei Normal University, Shijiazhuang 050024, China.
Wang SR; Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
Liu XY; Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
Fang WH; College of Chemistry and Material Science, Sichuan Normal University, Chengdu 610068, China.
Cui G; Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.

Molecules ; 28(10)2023 May 21.

Article en En | MEDLINE | ID: mdl-37241962

Palabras clave

excited states; machine learning; nonadiabatic couplings

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Molecules Asunto de la revista: BIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: China

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