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Article Dans Une Revue The Journal of Chemical Physics Année : 2018

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

Résumé

ABSTRACT 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. ACKNOWLEDGMENTS
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Dates et versions

hal-02906091 , version 1 (16-09-2022)

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David M. G. Williams, Wolfgang Eisfeld. Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces. The Journal of Chemical Physics, 2018, 149 (20), pp.204106. ⟨10.1063/1.5053664⟩. ⟨hal-02906091⟩
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