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

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

In this work, we implemented an approximate algorithm for calculating nonadiabatic coupling matrix elements (NACMEs) of a polyatomic system with ab initio methods and machine learning (ML) models. Utilizing this algorithm, one can calculate NACMEs using only the information of potential energy surfa...

Täydet tiedot

Bibliografiset tiedot
Päätekijät: Wen-Kai Chen, Sheng-Rui Wang, Xiang-Yang Liu, Wei-Hai Fang, Ganglong Cui
Aineistotyyppi: Artikkeli
Kieli:English
Julkaistu: MDPI AG 2023-05-01
Sarja:Molecules
Aiheet:
nonadiabatic couplings
machine learning
excited states
Linkit:https://www.mdpi.com/1420-3049/28/10/4222

Internet

https://www.mdpi.com/1420-3049/28/10/4222

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