Machine learning demonstrates the impact of proton transfer and solvent dynamics on CO₂ capture in liquid ammonia

Direct air capture of CO₂ using supported amines provides a promising means to achieve the net-zero greenhouse gas emissions goal; however, many mechanistic details regarding the CO₂ adsorption process in condensed phase amines remain poorly understood. This work combines machine learning potentials, enhanced sampling and grand-canonical Monte Carlo simulations to directly compute experimentally relevant quantities to elucidate the mechanism of CO₂ chemisorption in liquid ammonia as a model system. Our simulations suggest that CO₂ capture in the liquid occurs in a sequential fashion, with the formation of a metastable zwitterion intermediate. Furthermore, we identified the importance of solvent-mediated proton transfer and solvent dynamics, not only in the reaction pathway but also in the efficiency of CO₂ chemisorption. Beyond liquid ammonia, the methodology presented here can be readily extended to simulate amines with more complex chemical structures under experimental conditions, paving the way to elucidate the structure-performance of amines for CO₂ capture.