Haobo Li

Haobo Li’s research is dedicated to the integration of data science and energy science, with a specific focus on using AI tools to tackle intricate scientific challenges in energy catalysis. As the first work to use the AI method “sub-group discovery” to design two-dimensional materials supported single-atom catalysts (SACs), she performed a computational screening study of transition metal single-atom catalysts supported on vanadium disulfide (VS2). The single-atom catalysts were found to break the scaling relationship between the adsorption energies of intermediates on conventional metal or alloy catalysts and exhibit high activity and good selectivity towards nitrogen electroreduction.

To uncover valuable structural insights and elucidate the dynamic mechanisms involved in the reaction process, Li established a comprehensive structural database. Leveraging machine learning techniques, she employed an automated approach to select relevant structures from this database that best aligned with in-situ experimental spectra. This methodology aimed to extract pertinent structural information and shed light on the intricate dynamics of the reaction process.

Li extended computational modeling over a diverse set of active sites and developed the “active-site computational screening” concept and “active-site engineering” technology, with which she suggested a shift of focus toward more C-rich carbides for carbon dioxide reduction. The presence of active sites under the reaction conditions is another issue that needs to be considered. She performed ab initio thermodynamics to determine the stable surface phases of Mo2C in aqueous electrolyte and found the operand formation of ultrathin surface oxidic films, which have been verified in experiments. Furthermore, she has pioneered the concept of the electrochemical stability window as a means to characterize the stability of electrocatalysts during their operation under working conditions.