To learn and predict C-H hydricities, we train a LightGBM regressor with our QM dataset containing QM computed hydricities (740 compounds; 3278 hydricities). Then, we correlate and compare the ML predicted hydricities and the QM computed hydricities and achieve an MAE and RMSE of 2.30 kcal/mol and 3.74 kcal/mol for the held-out test set (145 compounds; 671 hydricities), as illustrated in Figure . For the ML predicted hydricities that are not correlating well with the QM computed hydricities, we find C-H sites where the positive charge is stabilized by resonance. We speculate that the ML model has seen few examples of the C-H sites that are outliers, and a more extensive training set would be advantageous. ALFABET is also compared against the test set, achieving an MAE and RMSE of 6.64 kcal/mol and 9.49 kcal/mol. Refer to section 5.1 in the Supporting Information for more details.