V. Diana Rakotonirina

arXiv2025

Hammett-Inspired Product Baseline for Data-efficient $Δ$-ML in Chemical Space

Data-hungry machine learning methods have become a new standard to efficiently navigate chemical compound space for molecular and materials design and discovery. Due to the severe scarcity and cost of high-quality experimental or synthetic simulated training data, however, data-acquisition costs can be considerable. Relying on reasonably accurate approximate legacy baseline labels with low computational complexity represents one of the most effective strategies to curb data-needs, e.g.~through $Δ$-, transfer-, or multi-fidelity learning. A surprisingly effective and data-efficient baseline model is presented in the form of a generic coarse-graining Hammett-Inspired Product (HIP) {\em Ansatz}, generalizing the empirical Hammett equation towards arbitrary systems and properties. Numerical evidence for the applicability of HIP includes solvation free energies of molecules, formation energies of quaternary elpasolite crystals, carbon adsorption energies on heterogeneous catalytic surfaces, HOMO-LUMO gaps of metallorganic complexes, activation energies for S$_\text{N}$2 reactions, and catalyst-substrate binding energies in cross-coupling reactions. After calibration on the same training sets, HIP yields an effective baseline for improved $Δ$-machine learning models with superior data-efficiency when compared to previously introduced specialised domain-specific models.

physics.chem-ph↗

arXiv2025

Combining Hammett $σ$ constants for $Δ$-machine learning and catalyst discovery

We study the applicability of the Hammett-inspired product (HIP) Ansatz to model relative substrate binding within homogenous organometallic catalysis, assigning $σ$ and $ρ$ to ligands and metals, respectively. Implementing an additive combination (c) rule for obtaining $σ$ 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 $Δ$-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, $Δ$-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.

physics.chem-ph↗