We have presented a methodology for calibrating a highthroughput computational chemistry technique (xTB-sTDA) against a high-accuracy one (TD-DFT) using machine learning. We first decided on Chemprop's directed message passing neural network (MPNN) as the ML architecture of choice, then generated a training set using literature scraping of relevant molecules from abstracts (SCOP-PCQC) and an existing excited state database (QM-symex-10k). We also generated an expanded training set using active learning. We built two models based on these training sets (xTB-ML-20k and xTB-ML-300k).