Another approach to computational catalyst discovery has focused on quantitative structureactivity relationships (QSAR) of organometallic catalysts (which represent the majority of homogenous catalysts). In the original approach, pioneered by Tolman in the 1970s , observed structural features of the catalysts and chemical features of the ligands are correlated with the observed catalytic activity. These QSAR models were then used to guide the discovery of new catalysts. As QM method became more powerful the observed features were augmented or replaced with QM-predicted features. With the advent of machine learning (ML) the QM and QSAR approaches are starting to merge. While the traditional QSARs had to be constructed based on expert chemical knowledge, ML models can learn these QSARs given enough data. Sufficient experimental reactivity data can be obtained for some reactions using high throughput techniques but in many cases, the catalytic activity is estimated using QM. A typical state-of-the-art computational organometallic catalyst discovery study (e.g. ) involves libraries of 10 4 -10 6 catalyst candidates constructed using predefined metals and ligands. The activity is then predicted for a small subset [O( 3 )] using QM based on a linear free energy relation between energies of key intermediates in the catalytic mechanism and the reaction rate. This data is then used to train an ML model that is then used to predict the reactivity of the entire library and the most interesting candidates are selected for further QM calculations. In some cases (e.g. ) the process is repeated, i.e. the new QM data is used to update the ML model and the library is re-screened. Another option is to use search algorithms (GAs), such as genetic algorithms, rather than evaluating the entire library. The efficiency of these search algorithms also allows for the use of QM, rather than ML, for reactivity. However, all studies so far have focused on screening user-defined libraries of catalysts. While experimental verification of catalysts predicted using these computational approaches are rare, Das et al. have recently successfully identified a frustrated Lewis pair catalyst for direct hydrogenation of CO 2 .