Deep Learning Potential Molecular Dynamics Simulation of Chemical Conversion-Absorption Coupling of CO₂ at Air-Reactive Deep Eutectic Solvent Interface

Capturing CO₂ from air is urgent to battle against the climate change. Traditional CO₂ capture by alkaline solution or aqueous amine has high regeneration cost, equipment corrosion, and amine leakage. Deep eutectic solvents (DESs) are promising alternatives with low corrosion, non-toxicity, and biodegradable nature. Functionalized DESs can capture and convert CO₂ at low partial pressures. Developing reactive DESs for efficient CO₂ capture needs a foundational understanding of the reaction-transport coupling of CO₂ at the air-DES interface. In this study, we develop a deep learning potential (DLP) to investigate the absorption and reaction of CO₂ at the air-DES interface (DES: 1-ethyl-3-methylimidazolium 2-cyanopyrrolide ([Emim][2-CNpyr]) and ethylene glycol (EG)). The DLP model allows us to simulate chemical reactions at a lower computational cost with ab initio accuracy. By analyzing reaction free energy surfaces, molecular interactions among CO₂, DES components and reaction products, and transport of CO₂ through the interface, we identify three main reaction pathways of CO₂ at the interface: forming carboxylate with [Emim], carbamate with [2-CNpyr], and carbonate with EG. The mechanistic understanding of CO₂ chemisorption at the interface will facilitate the development of novel direct air capture technology using DESs.