Machine Learning Light-Matter Interactions
The understanding of light-induced dynamics in molecules and nanoscale systems is often limited due to their inherent complexities. Are all these complexities essential that we take into consideration for solving the dynamics or provide enough transparency to gain insights? If there exists a subspace with minimal complexities encoding the essential dynamics then the problem is simplified significantly. But how do we explore that space if it exists at all? We combine quantum dynamics with machine learning methods to address this point.
Phys. Rev. Lett. 124, 113201 (2020) 🔗
Plasmon-Enhanced Light-Matter Interactions
Metal nanoparticles with dimensions of a few nanometers show efficient light absorption across the entire visible spectrum. The enhanced photo-excitation leads to the creation of highly excited non-equilibrium electron-hole pairs within a few femtoseconds of the excitation and subsequently, they thermalize to an equilibrium associated with an elevated electronic temperature that is higher than the lattice temperature via electron-electron scattering. The generated hot carriers and enhanced near-field induce dynamics for the molecules adsorbed on the surface (like enhanced spectroscopy, bond dissociation/formation, etc.). We study plasmon-enhanced chemical processes solving the real-time dynamics and modeling the effective dynamics through the Lindblad master equation under open quantum system frameworks. Furthermore, we study plasmon-enhanced nonlinear optical processes like second and higher-order harmonic generations.
Quantum Light Spectroscopy
We explore quantum advantages in spectroscopic problems of molecules under full quantum description i.e., treating both system and light quantum mechanically. The main motivation behind this is whether we can go beyond the fundamental limits of conventional classical light spectroscopy and control the nonlinear optical processes. One of the most important quantum signatures of light is the (energy-time) entanglement between photon pairs which can be generated through the spontaneous parametric downconversion (SPDC) process. Entangled light provides new opportunities for interrogating the properties of molecules, including information about excited states that cannot be obtained by other methods. To this end, we study electronic excitations of molecules modeling the quantum state of light and their interactions with molecules with quantum dynamics and/or machine learning methods.
Quantum Thermodynamics
The nontrivial geometric phase emerges in quantum heat engines as a consequence of periodic driving of bath temperatures or couplings. To this end, we understand the effect of the geometric phase in quantum thermodynamics and importantly control them with external driving through effective modeling of driven quantum heat engines.
Phys. Rev. E 96, 052129 (2017) 🔗
