Multistate Coupled Diabatic Neural Network potential for the quantum non-adiabatic Photofragmentation of CH ⁺ ₂

Pablo del Mazo‐Sevillano; Susana Gómez Carrasco; Alfredo Aguado; Octavio Roncero

doi:10.1039/d6cp01221c

January 1, 2026Physical Chemistry Chemical PhysicsOpen Access

Multistate Coupled Diabatic Neural Network potential for the quantum non-adiabatic Photofragmentation of CH ⁺ ₂

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Key Points

The aim is to develop a method for tracking non-adiabatic transitions in photodissociation of CH2+ accurately using neural networks.
Introduced an automated diabatization method to generate diabatic potential energy matrices (PEMs).
Focused on far-ultraviolet photodissociation and complex non-adiabatic transitions across excited states.
Utilized a multistate coupled neural network framework.
Successfully produced highly accurate PEMs for CH2+, improving modeling of its photodissociation.
Demonstrated the model's efficiency in capturing the dynamics of non-adiabatic transitions.

Abstract

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...

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Cite This Study

Mazo‐Sevillano et al. (2026) studied this question.

synapsesocial.com/papers/69fd7fa1bfa21ec5bbf081da https://doi.org/10.1039/d6cp01221c

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Multistate Coupled Diabatic Neural Network potential for the quantum non-adiabatic Photofragmentation of CH ⁺ ₂

Key Points

Abstract

Discussion

Cite This Study

Multistate Coupled Diabatic Neural Network potential for the quantum non-adiabatic Photofragmentation of CH ⁺ ₂

Key Points

Abstract

Discussion

Cite This Study

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