Similar to phosphine TEP values, there are several instances where CO stretching calculations were used to determine donor and acceptor effects for non-phosphine ligands. For example, Liu and Ke use DFT calculations to determine the steric and electronic parameters of N-heterocyclic boryl type ligands and Kuzu examined carbodiphosphorane ligands. It is useful to note that the use of TEP values has been slightly criticized by Cremer because they do not correlate with bond strength. However, regardless of this lack of correlation, TEP values allow comparison of phosphine electronic effects that can be potentially translated from the L-Ni(CO)3 complex to new complexes, and they can be used as one of many chemical descriptors in catalyst design. Therefore, our major goal was to develop a machine learning model from descriptors derived from simplified molecular-input line-entry system (SMILES) representations of phosphines that can then be used to instantaneously (at least compared to DFT) calculate accurate TEP values. Figure describes the approach of how this machine learning model, called TEPid, was developed. Using a combination of automated building and calculation tools we curated >4,000 DFT calculated (R)3P-Ni 0 (CO)3 TEP values.