  We study the applicability of the Hammett-inspired product (HIP) Ansatz to
model relative substrate binding within homogenous organometallic catalysis,
assigning $\sigma$ and $\rho$ to ligands and metals, respectively. Implementing
an additive combination (c) rule for obtaining $\sigma$ constants for any
ligand pair combination results in a cHIP model that enhances data efficiency
in computational ligand tuning. We show its usefulness (i) as a baseline for
$\Delta$-machine learning (ML), and (ii) to identify novel catalyst candidates
via volcano plots. After testing the combination rule on Hammett constants
previously published in the literature, we have generated numerical evidence
for the Suzuki-Miyaura (SM) C-C cross-coupling reaction using two synthetic
datasets of metallic catalysts (including (10) and (11)-metals Ni, Pd, Pt, and
Cu, Ag, Au as well as 96 ligands such as N-heterocyclic carbenes, phosphines,
or pyridines). When used as a baseline, $\Delta$-ML prediction errors of
relative binding decrease systematically with training set size and reach
chemical accuracy ($\sim$1 kcal/mol) for 20k training instances. Employing the
individual ligand constants obtained from cHIP, we report relative substrate
binding for a novel dataset consisting of 720 catalysts (not part of training
data), of which 145 fall into the most promising range on the volcano plot
accounting for oxidative addition, transmetalation, and reductive elimination
steps. Multiple Ni-based catalysts, e.g. Aphos-Ni-P($t$-Bu)$_3$, are included
among these promising candidates, potentially offering dramatic cost savings in
experimental applications.
