The current design of energy materials heavily relies on a time-intensive, trial and error research approach, which only yielded a few active structural motifs in the past decades and can no longer meet the rapidly increasing demand for sustainable high-performance materials. Our group uses data science and machine learning (ML) tools in combination with high‑throughput modeling and screening experiments to accelerate the discovery of electrochemical redox-active molecular structures for energy conversion and storage. Meanwhile, we are also interested in applied machine learning for assisted molecular simulation, electrochemical system optimization, catalyst design, and materials synthesis.

Hydrogen fuel production from renewable energy sources has been one of the main topics in electrochemistry. For more than a century, electrocatalysis has always relied on the use of metal-based catalysts, in which metal elements act as the catalytic center. Our research aims to develop high-performance electrocatalysts based on robust organic compounds, which will significantly reduce the material cost, and eliminate any durability issues associated with metal elements in harsh electrolyte conditions. In the meanwhile, we also look into the potential of energy storage capabilities of organic compounds.

Hydrogen peroxide (H₂O₂) is a low cost, safe, and strong oxidizer with versatile use in paper and pulp industry as a bleach, and in water treatment as disinfection agent. The global market is projected to reach 5.7 million metric tons per year by 2022. Current industrial production of H₂O₂ is through anthraquinone process at centralized facilities, with added cost and difficulties for storage and redistribution. We aim to develop low cost, distributed electrochemical devices to produce H₂O₂ on-site, for drinking water disinfection and medical disinfection during disaster mitigation

The increasing concentration of CO₂ in the atmosphere due to the consumption of fossil fuels is having severe effects on the climate, which will undoubtedly impact society. Electrochemical reduction of CO₂ is critical to achieving carbon-neutral fuel production using renewable energy and CO₂ sources from industrial exhaust or the environment. To date, state-of-the-art electrocatalysts for the CO₂ reduction reaction (CO2RR) heavily rely on the use of transition metal elements as active sites confined on 2D surfaces. This research aims to develop innovative three-dimensional molecular electrocatalysts based on organic molecules for CO2RR, targeting energy-dense multi-carbon liquid fuel.

Nucleation and growth fundamentally govern the formation of condensed phases in the universe. Crystallization from reactant solutions is a non-classical process that can not be fully explained by the classical theories, which describe the precipitation phenomena in saturated solutions. Our recent work suggests that the pre-nucleation chemical events direct the growth of nanocrystals from reactant solutions. This research aims to further understand the role of metal-ligand clusters at the early stage of nuclei formation, through the combination of ab initio calculation, Monte Carlo simulation and Molecular Dynamic modeling.