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Thèse Année : 2020

Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics

Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics

Résumé

The Born-Oppenheimer (BO) approximation is a cornerstone of the theoretical treatment of molecular processes. It separates nuclear and electronic motion, splitting up an immensely challenging many-body problem into two highly involved but well-understood ones. The electronic problem, solved for fixed nuclear geometries, yields eigenvalues of the electronic Hamiltonian as a function of the nuclear coordinates. Each eigenvalue (as a function of the nuclear coordinates) forms a potential energy surface (PES) for a given electronic eigenstate. The set of these electronic .....
The Born-Oppenheimer (BO) approximation is a cornerstone of the theoretical treatment of molecular processes. It separates nuclear and electronic motion, splitting up an immensely challenging many-body problem into two highly involved but well-understood ones. The electronic problem, solved for fixed nuclear geometries, yields eigenvalues of the electronic Hamiltonian as a function of the nuclear coordinates. Each eigenvalue (as a function of the nuclear coordinates) forms a potential energy surface (PES) for a given electronic eigenstate. The set of these electronic .....
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Dates et versions

tel-03917774 , version 1 (06-01-2023)

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  • HAL Id : tel-03917774 , version 1

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David M. G. Williams. Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics. Chemical Sciences. University of Bielefed, 2020. English. ⟨NNT : ⟩. ⟨tel-03917774⟩
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