Additionally, KNN had a comparable performance, with a slightly lower R 2 of 0.85, but a relatively low MAE of 4.01 ppm. The least accurate models were MLP with an R 2 of 0.51 and MAE of 13.18 ppm, as well as GPR with an R 2 of 0.4 and MAE of 12.95 ppm -15.11 ppm {Figure 4}. Furthermore, GBR, BR, RF, and KNN demonstrated superior performance by exhibiting close adherence to the ground truth line in the prediction plots. Conversely, the models GPR and MLP displayed a more dispersed distribution, indicating less accurate predictions {Figure 5}.We observed no trends within the performance of these models that could be attributed to inherent differences in the model family. To address the balance between efficiency and model performance, which we anticipated would vary with each dataset due to differences in atomic environmental complexity, we recorded the runtimes of each models' training and prediction process. Since the runtimes vary from model to model due to differences in architecture, we normalized the plotted runtimes for each model in the heatmap above for ease of interpretation, where we generally observed an exponential decrease between runtime and radii after this normalization. Expectedly, the 2 Å dataset had the fastest runtime overall among all the models. On the other hand, the superior model performance while using the 3 Å dataset (as shown through low MAE and values) led to the conclusion 𝑅 2 that the majority of models performed their best when trained on the 3 Å dataset. Therefore, we deduced that the optimal distance when retrieving local atomic environments of each fluorine atom is 3 Å.