Deep Neural Network Potential Demonstrates the Impact of Proton Transfer in CO2 Capture by Liquid Ammonia

The direct air capture of CO2 using aminopolymers can reduce the environmental impact caused by the still growing anthropogenic emissions of CO2 to the atmosphere. Despite the adsorption efficiency of aminopolymers even in ultradilute conditions, the mechanism of CO2 binding in condensed phase amines is still poorly understood. This work combines machine learning potentials, enhanced sampling and Grand Canonical Monte Carlo to directly compute experimentally-relevant quantities, such as the free energy and enthalpy of CO2 adsorption. Our free energy calculations elucidate the important role of solvent-mediated proton transfer on the formation of the most stable CO2-bound species: carbamate and carbamic acid. Liquid ammonia is used as a model system to study CO2 adsorption, but the methodology can be extended to amines with more complex chemical structure. The study of CO2 adsorption using machine learning brings computer simulations closer to the thermodynamic conditions of interest to experiments, thus paving the way to a more detailed study between the chemical composition of amines and their CO2 binding affinity.