Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics
Williams D (2020)
Bielefeld: Universität Bielefeld.
Dissertation | Englisch
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2020
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https://pub.uni-bielefeld.de/record/2956232
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Williams D. Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics. Bielefeld: Universität Bielefeld; 2020.
Williams, D. (2020). Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics. Bielefeld: Universität Bielefeld.
Williams, David. 2020. Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics. Bielefeld: Universität Bielefeld.
Williams, D. (2020). Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics. Bielefeld: Universität Bielefeld.
Williams, D., 2020. Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics, Bielefeld: Universität Bielefeld.
D. Williams, Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics, Bielefeld: Universität Bielefeld, 2020.
Williams, D.: Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics. Universität Bielefeld, Bielefeld (2020).
Williams, David. Conquering coupled diabatic potential energy surfaces with artificial neural networks for nonadiabatic dynamics. Bielefeld: Universität Bielefeld, 2020.
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Quantum dynamics and geometric phase in Ee Jahn-Teller systems with general Cnv symmetry
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The Journal of chemical physics 151(7): 074302.
Weike T, Williams D, Viel A, Eisfeld W (2019)
The Journal of chemical physics 151(7): 074302.
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Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces
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JOURNAL OF CHEMICAL PHYSICS 149(20): 204106.
Williams D, Eisfeld W (2018)
JOURNAL OF CHEMICAL PHYSICS 149(20): 204106.
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Complete Nuclear Permutation Inversion Invariant Artificial Neural Network (CNPI-ANN) Diabatization for the Accurate Treatment of Vibronic Coupling Problems
Williams D, Eisfeld W (2020)
Journal of Physical Chemistry A 124(37): 7608-7621.
Williams D, Eisfeld W (2020)
Journal of Physical Chemistry A 124(37): 7608-7621.
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Accurate quantum dynamics simulation of the photodetachment spectrum of the nitrate anion (NO3-) based on an artificial neural network diabatic potential model
Viel A, Williams D, Eisfeld W (2021)
The Journal of chemical physics 154(8): 084302.
Viel A, Williams D, Eisfeld W (2021)
The Journal of chemical physics 154(8): 084302.
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Diabatic neural network potentials for accurate vibronic quantum dynamics-The test case of planar NO3.
Williams D, Viel A, Eisfeld W (2019)
The Journal of chemical physics 151(16): 164118.
Williams D, Viel A, Eisfeld W (2019)
The Journal of chemical physics 151(16): 164118.
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