Quasi-infinite order vibronic coupling expansions with neural networks for linear systems: The C4H radical

We present a generic scheme for the development of coupled diabatic potential energy surfaces for Renner-Teller systems. The derived method re-expresses an infinite-order vibronic coupling expansion in terms of analytic functions of invariant coordinates, preserving the symmetry and structure of the vibronic coupling ansatz. Using feed-forward artificial neural networks as a flexible and well-established functional form, the formalism is applied to the C4H radical as a challenging (Σ+ + Π) ⊗ π system of astrochemical interest. Focusing on a 6D subspace of the most relevant modes, the neural network model is found to be in good agreement with computed ab initio data and significantly outperforms a comparable polynomial ansatz in terms of fitting error.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2025-07-14
DOI
https://doi.org/10.1063/5.0272604
Primary Topic
Advanced Chemical Physics Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Quasi-infinite order vibronic coupling expansions with neural networks for linear systems: The C4H radical

Alexandra Viel, D. R. M. Williams
The Journal of Chemical Physics
Advanced Chemical Physics Studies
article

Quasi-infinite order vibronic coupling expansions with neural networks for linear systems: The C4H radical

Alexandra Viel, D. R. M. Williams
article en

Abstract

We present a generic scheme for the development of coupled diabatic potential energy surfaces for Renner-Teller systems. The derived method re-expresses an infinite-order vibronic coupling expansion in terms of analytic functions of invariant coordinates, preserving the symmetry and structure of the vibronic coupling ansatz. Using feed-forward artificial neural networks as a flexible and well-established functional form, the formalism is applied to the C4H radical as a challenging (Σ+ + Π) ⊗ π system of astrochemical interest. Focusing on a 6D subspace of the most relevant modes, the neural network model is found to be in good agreement with computed ab initio data and significantly outperforms a comparable polynomial ansatz in terms of fitting error.

The Journal of Chemical PhysicsVol. 163(2)
Centre National de la Recherche Scientifique (FR), Institut de Physique de Rennes (FR), Université de Rennes (FR)
Agence Nationale de la Recherche, Centre National de la Recherche Scientifique, Université de Rennes 1, Horizon 2020 Framework Programme
Affordable and clean energy
Openalex Percentile: Top 87%
Advanced Chemical Physics Studies
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