We leverage machine learning, atomistic simulations, statistical mechanics, and mathematical modeling to gain a fundamental understanding of molecular adsorption, reaction, and transport in porous media, aiming to push the boundaries of discovery, design, and characterization of novel porous materials and confined systems. This is particularly important for applications in energy storage, chemical separation, catalysis, carbon capture, sensing, and nano-manufacturing. Our research group remains in close collaboration with experimental and theoretical groups in America, Europe, and Asia. Together, we solve some of the most outstanding global challenges in energy, healthcare, and sustainability.

We develop advanced machine learning, materials informatics, and molecular simulation methods to accelerate computational nanoporous materials discovery and design for clean energy and chemical separation applications.

We develop advanced theories and tools for experimental characterization of complex nanoporous materials. The goal is to enable efficient and interpretable materials discovery in self-driving lab and high-throughput experiments.

We develop realistic statistical mechanical theories and simulation methods to understand complex phase transitions and chemical reactions in confinement, with applications in crystallization and nano-manufacturing of drugs.