were also used as features to train a ridge regression ML model. The features were not transformed with any assumed logarithm or power. This model was applied to determine whether the proposed thermodynamic model offered any improvement over a typical linear regression -i.e. whether the functional form of the model carries any more meaning than the features themselves. A hyperparameter (α, chosen by the user) is used in the cost function to penalize the weights of the model coefficients, which effectively mitigates overfitting and variance. The α parameter is used to adjust the balance between overfitting (giving more features higher weights) and underfitting (not capturing enough of the variation in the data using the features). The model did not tend to overfit the training set (based on the error difference of 3-5 °C between the training and test sets 3), so an α value of 0.1 was chosen for model development.