Since our ML model predicts the error in xTB-sTDA, an interesting application is to map the error in S 1 and T 1 calculations in a global chemical space, to see if there are some areas where xTB systematically over-or under-estimates, or areas where xTB is projected to be fairly accurate. For this analysis, we use Class 1 xTB-ML-300k, as it is shown to be accurate in the general chemical domain and does not require xTB-sTDA computations, so large-scale predictions can be made quickly with ML.