RESEARCH
We are interested in theoretical and computational methods based on machine learning and quantum dynamics to study light-matter interactions at the nanoscale. Our research addresses fundamental light-matter interactions with potential applications in molecular quantum nanophotonics.
Machine Learning Light-Matter​
​
Theoretical and computational modeling of light induced processes in molecules and nanoscale systems is often challenging, primarily due to the high dimensionality of the underlying dynamics. In many cases, this complexity not only leads to higher computational costs but also limits interpretability. However, are such complexities indispensable for capturing the essential dynamics and explaining experimental outcomes? If a reduced subspace exists - one that retains the underlying dynamics while minimizing complexity - it could provide a powerful framework for constructing simplified and interpretable models. To explore this possibility, we combine quantum dynamics with machine learning to identify and exploit such low dimensional representations.
​​"The purpose of models is not to fit the data but to sharpen the questions."
— Samuel Karlin
​
"Everything should be made as simple as possible, but not simpler."
— Albert Einstein
Nanoscale Light-Matter
​
​
Noble metal nanoparticles provide a potential platform for manipulating light-matter interactions at the nanoscale, owing to their wavelength tunable light absorption through surface plasmon resonances. We explore the quantum chemical dynamics that emerge at the interfaces of these plasmonic structures, where plasmons can strongly interact with nearby molecules. Our research aims to uncover the quantum aspects of such light-matter interactions - with a particular emphasis on the understanding of the influence of vacuum fluctuations, quantum coherence, and entanglement on molecular processes. To model these effects, we develop and apply methods from open quantum dynamics and machine learning, enabling simulations of quantum emitters, photocatalysis, charge and energy transfer processes.
Nonlinear Light-Matter
​
We study nonlinear spectroscopy of organic molecules under a fully quantum description, treating both molecules and light quantum mechanically. One of the main objectives of this treatment is to overcome the fundamental limits of conventional light spectroscopy by leveraging the quantum nature of light. Entangled photons have shown promise in enhancing two-photon absorption and improving photochemical reaction efficiencies. We develop methods based on open quantum dynamics and machine learning to broaden the fundamental understanding of entangled light-matter interactions and their connection with nonlinear spectroscopy.
