The weights 'w i ' in equation 1 serve as hyperparameters to balance the influence of each MSE term during training. These hyperparameters play a crucial role in adjusting the weights of individual MSE terms, considering their varying magnitudes and importance in the learning process. For each layer of the Feedforward Neural Network, we use the Leaky ReLU activation function, which effectively handles vanishing gradient problems . The network is trained to minimize the loss(L) using ADAM, a stochastic gradient descent method. In equation 2, U pred and S pred are the predicted values of energy and entropy respectively. Whereas F obs are the observed values of Free energy. All the network hyperparameters were optimized to maximize the coefficient of determination R 2 for the Free Energy. Details of the hyperparameter optimization are provided in the supplementary information. The codes are made available in