Carbon Dioxide Electroreduction: Toward Sustainable Carbon Utilization
Electrochemical CO₂ reduction (CO₂ER) provides a promising pathway to transform CO₂ into fuels, alcohols, and other valuable chemicals—offering a route toward sustainable carbon utilization. A major challenge lies in designing electrolytes that efficiently dissolve CO₂, enable rapid ion transport, and ensure stable electrochemical operation.
Our project employs a data-driven, high-throughput workflow that integrates quantum chemistry, thermodynamic modeling, molecular dynamics, and machine learning to accelerate electrolyte discovery for CO₂ER. We combine DFT, COSMO-RS, and molecular dynamics simulations to evaluate key electronic, thermodynamic, and structural properties of CO₂ER electrolytes. Machine learning models extend these predictions across chemical space and, through SHAP analysis, reveal the molecular features driving optimal electrolyte performance.
This integrated workflow culminates in the COSMIC database—a curated, open-source repository of electrolyte candidates enriched with detailed physicochemical and transport properties, forming a robust foundation for data-driven screening and experimental validation in CO₂ electroreduction research.