Combining all of the above methodology, a workflow was developed to create ML models that calibrate the xTB-sTDA energy of molecules against TD-DFT reference. The workflow can be separated into three distinct steps: data generation, model training, and model testing. In the data generation step, S 1 and T 1 excited state energies using TD-DFT and sTDA were either extracted from existing databases or calculated, if necessary. The errors between the energies derived from the two techniques were calculated and used as the groundtruth values that the ML model tried to predict. The SMILES strings were also extracted for molecules and used as a representation of molecular structure.