The combination of machine learning, enhanced sampling and Grand Canonical Monte Carlo allows a direct parallel between simulations and CO 2 adsorption experiments. The methodology developed in this paper provides experimentally accessible quantities, such as the free energy and enthalpy of CO 2 adsorption. Moreover, the minimum free energy path from reactants to products evidenced the critical role of proton transfer (mediated by the solvation environment) on the formation of the most stable CO 2 -bound species: the carbamate and the carbamic acid. Although the present paper focused on CO 2 adsorption in liquid ammonia, the proposed methodology should be equally applicable to study more complex systems. The future introduction of more complex systems to the DNN training data will allow a atomic-level understanding of the chemical composition of the amines on the CO 2 adsorption efficiency of amine-based systems.