Once the ML model was trained, it was tested on various datasets. Using the respective inputs, the ML model predicted xTB-sTDA vs. TD-DFT errors for the test molecules. When the errors were added to the original xTB-sTDA values, the final calibrated energies were obtained. These were compared to the TD-DFT-calculated values to get a quantitative measure of accuracy of each ML model.