we set n = 2 Å, 3 Å, 4 Å, and 5 Å in four independent data processing rounds, yielding four different datasets with varying "neighbor" sets for each fluorine atom. Additionally, for every "neighbor," we included its atomic number and charge (from the Ghemical files), We trained and evaluated the following regression models: Gradient Boosting Regressor (GBR) , Gaussian Process Regressor (GPR) , Decision Trees (DT) , Random Forest Regression (RF) , Extra Trees (ET) , Adaboost (ADB) , Bagging Regression (BR) , Lasso Regression (LSO) , Ridge Regression (RDG) , Elastic Net (EN) , Multi-layer Perceptron Regressor (MLP) , Support Vector Regression (SVR) , and K-Nearest Neighbors (KNN) {Figure 2}. All models were implemented in Python using the scikit-learn library . Each model's hyperparameters were optimized using the Bayesian optimization algorithm from a combination of Python's Bayesian optimization library and scikit-learn's Bayes Search CV. Hyperparameter search space bounds were manually defined. In use cases of the Bayesian optimization library, the objective functions were written to optimize by either mean absolute error (MAE) or R 2 . The accuracy of each model in predicting DFT-calculated 19 F NMR chemical shifts was evaluated using MAE and R 2 .