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Stochastic Electronic Structure Theory

We delve into the realm of ab initio methods, pivotal in uncovering the properties of molecules and materials. These methods, aiming to solve intricate Schrödinger equations, encounter challenges for larger systems due to computational demands. Our focus lies in advancing stochastic electronic structure techniques, leveraging random vectors to create a novel low-rank approximation. By doing so, we achieve linear scaling for Density Functional Theory (DFT) and TD-DFT, holding the promise to model complex materials like nanostructures and interfaces. This innovation has the potential to expand computational materials science horizons, bridging the gap for sizable experimental systems.

Molecular Dynamics Simulation with Machine Learning

Molecular Dynamics (MD) simulations are pivotal for understanding materials, biomolecules, and chemical reactions. Despite advanced computational power, these simulations are constrained by time scales, often limited to milliseconds due to molecular entrapment. To tackle this, enhanced sampling techniques have emerged, blending physical laws, chemistry, and data science. We focus on leveraging modern data science to extract insights from MD trajectories. This aids in diverse molecular conformation sampling by integrating machine learning-driven collective variables and creating coarse-grained models. Predicting protein configurations using all-atom MD simulations through machine learning is also a core aspect of our research.

Thermodynamics with strong light-matter interaction

The historical interplay between light and matter, spanning molecules to condensed matter, is harnessed through techniques like spectroscopy across disciplines. While often theoretically approached with "weak coupling," allowing perturbative study, pronounced light-matter interactions occur in optical cavities, necessitating integrated modeling. Our research delves into understanding strong light-matter interactions, altering ground state properties in multi-molecule systems. Using machine learning, we model these interactions' effects on intermolecular dynamics, coupled with molecular dynamics simulations revealing structural and thermodynamic insights within optical cavities.

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