The LightGBM Regressor was used for the machine learning model with hyperparameters optimized using the Optuna algorithm. Optuna performs automated searches for hyperparameters based on a chosen function to either minimize or maximize. In the case of this application, the 5-fold crossvalidation average root-mean-square error across the training set was chosen to be minimized. The 4073 phosphines were split into a training and testing sets with a 70:30 split with 3292 training structures and 1411 testing structures.