Since many of the previously mentioned features are redundant, a selection process is conducted using the Random Forest algorithm to optimize the feature space. Subsequently, the selected features are fed into a Feedforward Neural Network (FNN). The penultimate layers of this FNN are trained on the energy and entropy values of the Thermodynamic Free Energy equation: F=U-TS. Here, 'T' represents the temperature of the system. Furthermore, to constrain the network to this equation we train it using a modified loss function described below. This loss function is formulated as a linear combination of three Mean Square Error (MSE) terms, namely MSE U , MSE S , and MSE T hermo .