In our previous work, we demonstrated that the ∆-ML approach 61-65 could enhance the accuracy of predictions by a factor of four compared to direct-ML. Here, direct learning refers to modeling the high-level binding energy (here denoted as ∆E high ). ∆-ML models the difference (∆∆E) between a slow, high-level theory (∆E high ) and a fast, low-level theory (e.g., GFN1-xTB, here denoted as ∆E low ),