Before using an ML model, the descriptors were standardized using the scikit-learn StandardScaler to ensure that the descriptors are of the same magnitude. The input data was then divided into a training set with 80% of the data and a test set with the remaining 20%. The following sci-kit learn regressor models were examined, each with their respective hyper-parameters carefully tuned: LASSO, random forest (RF), extreme gradient boosting (XGB) random forest, support vector regression (SVR), K-nearest neighbors (KNN), lightGBM, and multi-neuron perceptron (referred to as NN). The ensemble models between LASSO and all other models and between RF and all other models are evaluated using a voting regressor. The performance of each model was evaluated by 5-fold cross-validating the predictions of each model. The R 2 , mean absolute error (MAE), and root mean squared error (RMSE) were evaluated as the mean of the 5-fold cross-validated scores for each model.