Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces

Williams D, Eisfeld W (2018)
JOURNAL OF CHEMICAL PHYSICS 149(20): 204106.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
Abstract / Bemerkung
A new diabatization method based on artificial neural networks (ANNs) is presented, which is capable of reproducing high-quality ab initio data with excellent accuracy for use in quantum dynamics studies. The diabatic potential matrix is expanded in terms of a set of basic coupling matrices and the expansion coefficients are made geometry-dependent by the output neurons of the ANN. The ANN is trained with respect to ab initio data using a modified Marquardt-Levenberg back-propagation algorithm. Due to its setup, this approach combines the stability and straightforwardness of a standard low-order vibronic coupling model with the accuracy by the ANN, making it particularly advantageous for problems with a complicated electronic structure. This approach combines the stability and straightforwardness of a standard low-order vibronic coupling model with the accuracy by the ANN, making it particularly advantageous for problems with a complicated electronic structure. This novel ANN diabatization approach has been applied to the low-lying electronic states of NO3 as a prototypical and notoriously difficult Jahn-Teller system in which the accurate description of the very strong non-adiabatic coupling is of paramount importance. Thorough tests show that an ANN with a single hidden layer is sufficient to achieve excellent results and the use of a " deeper" layering shows no clear benefit. The newly developed diabatic ANN potential energy surface (PES) model accurately reproduces a set of more than 90 000 Multi-configuration Reference Singles and Doubles Configuration Interaction (MR-SDCI) energies for the five lowest PES sheets. Published by AIP Publishing.
Erscheinungsjahr
Zeitschriftentitel
JOURNAL OF CHEMICAL PHYSICS
Band
149
Ausgabe
20
Art.-Nr.
204106
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Williams D, Eisfeld W. Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces. JOURNAL OF CHEMICAL PHYSICS. 2018;149(20): 204106.
Williams, D., & Eisfeld, W. (2018). Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces. JOURNAL OF CHEMICAL PHYSICS, 149(20), 204106. doi:10.1063/1.5053664
Williams, D., and Eisfeld, W. (2018). Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces. JOURNAL OF CHEMICAL PHYSICS 149:204106.
Williams, D., & Eisfeld, W., 2018. Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces. JOURNAL OF CHEMICAL PHYSICS, 149(20): 204106.
D. Williams and W. Eisfeld, “Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces”, JOURNAL OF CHEMICAL PHYSICS, vol. 149, 2018, : 204106.
Williams, D., Eisfeld, W.: Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces. JOURNAL OF CHEMICAL PHYSICS. 149, : 204106 (2018).
Williams, David, and Eisfeld, Wolfgang. “Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces”. JOURNAL OF CHEMICAL PHYSICS 149.20 (2018): 204106.

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