The use of transition metal catalyzed carbon-carbon (e.g., Suzuki-Miyaura coupling ) and carbon-heteroatom (e.g., Buchwald-Hartwig amination ) cross-coupling reactions is foundational in modern synthetic chemistry including the synthesis of pharmaceuticals, agrochemicals, and materials. By far the most commonly used transition metal catalyst used in these reactions is palladium where the reactivity and selectivity of Pd complexes can be tuned over a wide range by selecting appropriate ligands. Identifying ligands that tailor the steric and electronic properties of the organometallic complex to achieve the most selective or highest yielding reaction outcomes for a given substrate is a central task in the application of Pd-catalyzed cross-coupling reactions. A notable difficulty is to optimize or overcome so-called molecular volcano relationships, where descriptors, such as free energy barriers of forming an intermediate, are linearly related to the energetics or turnover of the catalytic cycle. Unfortunately, extracting simple molecular catalyst design rules based on chemical identity of the ligating species is not straightforward. As a result, data-driven platforms focused on ligand discovery, such as Kraken for organophosphorus ligands , and other computational tools have been introduced. Considering a broader scope of chemical diversity, millions of candidate Pd-based catalysts can be readily envisioned, requiring the analysis of millions-to-billions of reactions if complete multi-step cycles and conformational sampling are used in catalyst evaluation. This number is far too large to be characterized by experiments or conventional quantum chemistry alone. Predicting catalyst performance both efficiently and accurately is a critical need that our current study aims to address.