Computational Spectroscopy of Condensed-Phase Systems
Vibrational spectroscopy is a powerful and sensitive tool to study structure and dynamics of many condensed-phase systems. Our group develops new and improves existing theoretical methods for linear and nonlinear infrared, sum-frequency generation, and Raman spectroscopies of condensed-phase systems of biological and technological importance such as liquid water, ice, ionic liquids, proteins in solution, on surfaces and at the interfaces.
Strong light–matter interactions provide a novel approach toward modifying molecular properties by forming hybrid light–matter states-known as polaritons. These polariton states have delocalized excitations among the molecules and cavity mode and constitute new strategy to control chemistry. We develop new theoretical methods and computational algorithms to understand the dynamics of exciton-polariton systems in the presence of the energetic disorder and dissipative environment in non-perturbative and non-Markovian regimes.
Machine Learning for Quantum Dissipative Dynamics
Our group develops various machine learning approaches to a multitude of problems in atomic and molecular physics. Specifically, we utilize neural networks to establish structure-property relationships for computational spectroscopy, many-electron perturbation theory, and to analyze the diffraction data. We also use neural networks to study reduced dynamics of open quantum systems.