We then extracted dozens of connectivity-based descriptors associated with SMILES strings from the (R)3P-Ni 0 (CO)3 complexes. We then optimized regressor-type machine learning models and pruned the descriptors to the 19 key model features. This TEPid machine learning model is extremely fast and provides an error of less than 1 cm -1 . Additionally, we built a web-based interface to run the machine learning model where SMILES strings can be entered and TEP values returned. We then applied this TEPid model to examine the donor and acceptor capability of phosphines in the large Kraken phosphine database. This revealed that the Kraken database is somewhat skewed towards donor phosphines. In the same spirit of Kraken, we generated new phosphines that when used with Kraken provide a ligand library that is electronically more balanced.