First-principles molecular dynamics (FPMD) should in principle be able to model the reactive adsorption of CO 2 in condensed phase. However, the high computational cost of FPMD limits the system size to a couple hundred atoms and the simulation time on the order of tens of picoseconds. This time scale is, in most cases, insufficient to properly converge the free energy surface of chemical reactions. Recent advances in machine learning (ML) have significantly decreased computational cost of FPMD without significant loss of accuracy. ML interatomic potentials have already been extensively applied to study chemical reactions, phase transitions, vibrational spectroscopy, all demonstrating good agreement with first-principles calculations and/or experimental observations. This work provides a new computational framework to study the reactive adsorption of CO 2 into condensed phase amines. The free energy of the chemical reaction is directly computed at finite temperature and pressure using molecular dynamics in conjunction with enhanced sampling techniques. The chemical potential of CO 2 in liquid ammonia is obtained by Grand Canonical Monte Carlo (GCMC). The desorption temperature and head of adsorption are obtained through calculation of CO 2 binding free energy as a function of tempereture. The potential energy surface of the system is described via a machine learning (ML) potential trained to reproduce the potential energy surface derived from electronic structure theory. While in this paper we have used liquid ammonia as a model system, the present methodology can be readily applied to study the thermodynamics of CO 2 adsorption in amines with more complex chemical structures.