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This paper introduces a Hermitian formalism based on the linear vibronic coupling (LVC) model to effectively describe the quantum dynamics of plasmonic excitations in metallic nanostructures. Utilizing first-principles calculations and machine learning via the Python Plasmonic Cavity (PyPC) platform, the authors successfully reproduce experimental absorption spectra and vibronic broadening for large silver nanoparticles. Key findings reveal that the LVC model not only predicts ultrafast lifetimes for bright states but also captures the dynamics of dark states, addressing significant challenges in the field.
The LVC model reveals that dark states in plasmonic systems can be dynamically characterized, challenging traditional views on their role in quantum dynamics.
Modeling the quantum dynamics of plasmonic excitations -- collective oscillations of free electrons interacting with light -- remains a significant theoretical challenge, particularly due to the need to accurately describe their quantum nature and the role of non-radiative decay channels. At the same time, a reliable theoretical framework is essential for advancing applications ranging from materials design to the development of new quantum optical platforms for quantum technologies. In this work, we address these challenges by introducing a Hermitian formalism based on the linear vibronic coupling (LVC) model for the description of plasmonic excitations in metallic nanostructures. This is parameterized through first-principles calculations -- including but not limited to, the full DFT ground state with tight-binding excited states -- and machine learning techniques using a newly implemented automated platform named Python Plasmonic Cavity (PyPC). The effectiveness of this workflow is demonstrated by successfully reproducing the experimental absorption spectra and vibronic broadening of plasmonic silver nanoparticles containing more than a hundred atoms. Additionally, the population dynamics of plasmonic states are investigated, showing that the LVC model accurately predicts ultrafast lifetimes for bright states and effectively captures the dynamics of dark states.
