My primary research interests lie in the development and application of multiscale modeling methods, specifically density functional theory (DFT) coupled with machine learning (ML), to address pressing scientific challenges related to sustainable chemical production and energy conversion & storage. Through my research, I aim to better understand, design, and discover next-generation heterogeneous catalysts and functional materials. Some noteworthy contributions to this field include:​

• Demonstrated electric dipole-related properties as effective descriptors for catalytic surface-adsorbates interactions (J. Am. Chem. Soc., 2020, 142, 7737). The proposed descriptors were recommended as a “promising new type of catalytic descriptor” on Science, 2020, 368, 727 as the “editor’s choice”.​

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• ​For the first time, established interpretable machine-learned mathematical formulas to quantitatively determine catalytic-related properties from vibrational spectroscopy (J. Am. Chem. Soc., 2022, 144, 16069). This study dramatically broadens the utility of spectroscopic tools in the context of catalysis.

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• Exploited 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). The effectiveness of this approach has been experimentally validated, with many of the predicted perovskites outperforming the previous benchmark by a factor of >2 (Adv. Energy Mater., 2023, 2203833).