Our lab specializes in developing and applying advanced computational techniques, including density functional theory (DFT) and machine learning (ML), to address complex challenges in chemistry and materials science. We focus on the intersection of quantum chemistry, statistical mechanics, and data-driven models to predict molecular properties, optimize catalytic processes, and design innovative materials. Key areas of research include:
- Molecular Property Prediction: Utilizing deep learning and uncertainty quantification methods to improve the accuracy and interpretability of molecular property predictions, with applications in materials development and chemical engineering.
- Catalysis and Reaction Mechanisms: Investigating the reaction mechanisms of heterogeneous and homogeneous catalysts, particularly in biomass conversion and complex reactions within porous materials such as metal-organic frameworks (MOFs) and zeolites, to enhance reaction efficiency and sustainability.
- Machine Learning in Chemical Kinetics: Creating machine learning models to accelerate the study of reaction kinetics and thermodynamic properties, with an emphasis on renewable energy and environmental solutions.
By integrating advanced computational methods with experimental validation, our research strives to deepen the understanding of chemical processes and deliver practical innovations for sustainable energy, green chemistry, and environmental protection.