Tolman Electronic Parameter Predictions from a Fast, Accurate, and Robust Machine Learning Model Provide Insight into Phosphine Ligand Electronic Effects

Phosphines are extremely important ligands in organometallic chemistry and their donor or acceptor ability can be measured through the Tolman electron parameter (TEP). Here we describe the development of a TEP machine learning model (called TEPid) that provides nearly instantaneous calculation of experimentally calibrated CO vibrational stretch frequencies for (R)3P-Ni0(CO)3 complexes. This machine learning model with an error of less than 1 cm-1 was developed using >4,000 DFT calculated (R)3P-Ni0(CO)3 TEP values and 19 key connectivity-based descriptors associated with SMILES strings. We also built a web-based interface to run the machine learning model where SMILES strings can be entered and TEP values returned. We applied this TEPid model to examine the donor and acceptor capability of phosphines in the large Kraken phosphine database. Surprisingly, this showed that the Kraken database is skewed towards donor phosphines. In the same spirit of the Kraken database, we generated tens of thousands of new experimentally based phosphines that when combined with Kraken phosphines provide a more electronically balanced ligand library.