My research interests mainly focus on developing and exploiting multiscale modeling methods such as density functional theory (DFT), combined with data-driven techniques like machine learning (ML) to help understand, design, and discover next-generation heterogeneous catalysts and functional materials for a broad range of pressing scientific issues related to sustainable chemical production and energy conversion and storage. My past research works include significant contributions to a wide variety of interdisciplinary projects, for example:
1) Exploiting ML neural network to develop electric dipole-related descriptors for catalytic surface-adsorbates interactions (J. Am. Chem. Soc., 2020, 142, 7737-7743). The proposed descriptors were recommended as a “promising new type of catalytic descriptor” on Science, 2020, 368, 727-728 as the “editor’s choice”.
2) For the first time, establishing interpretable machine-learned mathematical formulas to quantitatively determine catalytic-related properties from vibrational spectroscopy (J. Am. Chem. Soc., 2022, 144, 16069–16076). This study dramatically broadens the utility of spectroscopic tools in the context of catalysis.
3) Exploiting high-throughput DFT + ML to accelerate perovskite-type catalysts discovery for a wide range of chemical looping applications, such as air separation, CO2/H2O splitting, and methane partial oxidation (Energy Environ. Sci., 2022, 15, 1512–1528). The effectiveness of this approach has been experimentally validated, with many of the predicted perovskites outperforming the previous benchmark by a factor of >2.
The long-term vision of my future research group is to integrate computational modeling (e.g., DFT) and data-driven techniques (e.g., machine learning) to help understand and design advanced catalysts. Our research will cover a broad range of pressing fundamental and applied scientific issues, providing in-silico solutions to the major challenges pertaining to sustainable chemical production and energy conversion & storage.