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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.04756 |
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| _version_ | 1866913094512410624 |
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| author | del Mazo-Sevillano, Pablo Gomez-Carrasco, Susana Aguado, Alfredo Roncero, Octavio |
| author_facet | del Mazo-Sevillano, Pablo Gomez-Carrasco, Susana Aguado, Alfredo Roncero, Octavio |
| contents | Tracking the complex non-adiabatic transitions in far-ultraviolet photodissociation demands highly accurate
diabatic potential energy matrices (PEMs) across numerous excited states. To address this, we introduce a fully automated
diabatization method that leverages artificial neural networks to fit PEMs.
Our approach divides the PEM into a physically grounded zeroth-order diagonal term, which is then corrected by a neural network matrix to capture electronic couplings.
By enforcing symmetry constraints on off-diagonal elements and sharing degenerate diabatic states between the $A'$ and $A''$ irreducible representations,
the { diabatization} process becomes completely automatic.
We validate this method using time-dependent wavepacket calculations to simulate the photodissociation of CH$_2^+$, incorporating relevant states up to $\approx 13.6$~eV.
Finally, we compute partial cross-sections for all fragmentation channels -- including total and partial fragmentation yielding
\ce{CH+}, \ce{CH}, \ce{H2}, and \ce{H2+} diatoms -- revealing a notably high cross-section for the formation of the \ce{CH} radical. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04756 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multistate Coupled Diabatic Neural Network potential for the quantum non-adiabatic Photofragmentation of CH$_2^+$ del Mazo-Sevillano, Pablo Gomez-Carrasco, Susana Aguado, Alfredo Roncero, Octavio Chemical Physics Tracking the complex non-adiabatic transitions in far-ultraviolet photodissociation demands highly accurate diabatic potential energy matrices (PEMs) across numerous excited states. To address this, we introduce a fully automated diabatization method that leverages artificial neural networks to fit PEMs. Our approach divides the PEM into a physically grounded zeroth-order diagonal term, which is then corrected by a neural network matrix to capture electronic couplings. By enforcing symmetry constraints on off-diagonal elements and sharing degenerate diabatic states between the $A'$ and $A''$ irreducible representations, the { diabatization} process becomes completely automatic. We validate this method using time-dependent wavepacket calculations to simulate the photodissociation of CH$_2^+$, incorporating relevant states up to $\approx 13.6$~eV. Finally, we compute partial cross-sections for all fragmentation channels -- including total and partial fragmentation yielding \ce{CH+}, \ce{CH}, \ce{H2}, and \ce{H2+} diatoms -- revealing a notably high cross-section for the formation of the \ce{CH} radical. |
| title | Multistate Coupled Diabatic Neural Network potential for the quantum non-adiabatic Photofragmentation of CH$_2^+$ |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2605.04756 |