The CALPHAD method is a principal method for phase diagram and molten salt database development. Experimental thermodyanmic data (from phase equilibria measurements and activities of solution species) and/or AIMD simulations are used to empirically fit models and then predict stable phases at different temperatures or compositions. As mentioned above it would be difficult, expensive, and time-consuming to obtain data for highdimensional salt systems through either experiments or AIMD simulations. The chemical potential is central for understanding molten salt thermodynamic properties and predicting phase behavior. Studies of the chemical potential are limited due to the abovementioned difficulties, however. The classical PIM potential force field has been used to calculate the activity coefficient ratios for lanthanide cations (e.g. U +3 to Y +3 in the LiCl/KCl eutectic mixture ) and the free energy change for the reaction of the Eu +3 /Eu +2 redox couple in molten KCl. The Widom particle insertion method has been employed with AIMD simulations to calculate the chemical potential and the solubility of the sodium atom in molten NaCl. The cutting-edge NNIP-MD methods hold significant promise to play a vital role in exploring the thermodynamic properties since they provide quantum-level accuracy with efficiency similar to classical simulations. In the present work, molten NaCl is chosen as a prototype system to demonstrate the validity of the NNIPbased quasichemical theory (QCT) calculations, which is shown to be an efficient approach for directly calculating the solute ion solvation free energy via molecular dynamics simulations. To our knowledge, this is the first application of deep learning methods to the calculation of excess thermodynamic properties of molten salts.