Four different commonly used ML models -linear regression (baseline), random forest (RF) , Gaussian processes (GP), and artificial neural networks (ANN), were compared for a regression task and the effect of the input features on model performance was assessed with the goal of determining whether we could boost model performance with hand-crafted DFT based descriptors. The investigation began with simple OHE where the model is not given any chemical information. Subsequently, more features such as fingerprints/DFT descriptors were added to evaluate how a richer source of chemical information influences the model performance. Hybrid features were also generated, consisting of combinations of steric descriptors, electronic descriptors, OHE and principal components of Morgan 2 fingerprints.