3. Results and Discussion were able to identify 265 unique Pd catalysts that are predicted to have an energy difference in the target range using an ML model to exhaustively screen the same library we search with this GA. Their ML model was trained on a total of 7054 molecules out of which 2595 contained Pd. If they restricted themselves to only Pd-containing catalysts and were able to obtain a model performing similarly on Pd-containing catalysts, they would have been able to find 265 catalysts while doing 2 • 2595 = 5190 DFT optimization (two for each molecule). This highlights how GAs can be used in connection with DFT for scoring to search a large pre-defined library of ligands with comparably little computational cost.