Back to Search
Start Over
Diabatic neural network potentials for accurate vibronic quantum dynamics -The test case of planar NO3
- Source :
- Journal of Chemical Physics, Journal of Chemical Physics, American Institute of Physics, 2019, 151 (16), pp.164118. ⟨10.1063/1.5125851⟩, Journal of Chemical Physics, 2019, 151 (16), pp.164118. ⟨10.1063/1.5125851⟩
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- International audience; A recently developed scheme to produce high-dimensional coupled diabatic potential energy surfaces (PESs) based on artificial neural networks (ANNs) [D. M. G. Williams and W. Eisfeld, J. Chem. Phys. 149, 204106 (2019)] is tested for its viability for quantum dynamics applications. The method, capable of reproducing high-quality ab initio data with excellent accuracy, utilizes simple coupling matrices to produce a basic low-order diabatic potential matrix as an underlying backbone for the model. This crude model is then refined by making its expansion coefficients geometry-dependent by the output neurons of the ANN. This structure, strongly guided by a straightforward physical picture behind nonadiabatic coupling, combines structural simplicity with high accuracy, reproducing ab initio data without introducing unphysical artifacts to the surface, even for systems with complicated electronic structure. The properties of diabatic potentials obtained by this method are tested thoroughly in the present study. Vibrational/vibronic eigenstates are computed on theX andà states of NO 3 , a notoriously difficult Jahn-Teller system featuring strong nonadiabatic couplings and complex spectra. The method is investigated in terms of how consistently it produces dynamics results for PESs of similar (fitting) quality and how the results depend on the ANN size and ANN topography. A central aspect of this work is to understand the convergence properties of the new method in order to evaluate its predictive power. A previously developed, high-quality model utilizing a purely (high-order) polynomial ansatz is used as a reference to showcase improvements of the overall quality which can be obtained by the new method.
- Subjects :
- Physics
[PHYS]Physics [physics]
010304 chemical physics
Quantum dynamics
Diabatic
Ab initio
Vibronic couplings
General Physics and Astronomy
010402 general chemistry
01 natural sciences
Potential energy
0104 chemical sciences
[CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistry
Vibronic coupling
Matrix (mathematics)
MCTDH
0103 physical sciences
Artificial Neuron Networks ANN
Statistical physics
[PHYS.PHYS.PHYS-CHEM-PH]Physics [physics]/Physics [physics]/Chemical Physics [physics.chem-ph]
Physical and Theoretical Chemistry
Molecular physics
Eigenvalues and eigenvectors
Ansatz
Subjects
Details
- Language :
- English
- ISSN :
- 00219606 and 10897690
- Database :
- OpenAIRE
- Journal :
- Journal of Chemical Physics, Journal of Chemical Physics, American Institute of Physics, 2019, 151 (16), pp.164118. ⟨10.1063/1.5125851⟩, Journal of Chemical Physics, 2019, 151 (16), pp.164118. ⟨10.1063/1.5125851⟩
- Accession number :
- edsair.doi.dedup.....35f5a8b3e17a0e99dbe786c318a9736f