Repurposing quantum chemical descriptor datasets for on-the-fly generation of informative reaction representations: application to hydrogen atom transfer reactions

Javier E. Alfonso-Ramos, Rebecca M. Neeser, Thijs Stuyver (2024)
Highlighted by Jan Jensen



If you have very little data, the single most useful thing you can do is find good descriptors. Sigman, Doyle, and others have shown this very nicely for reactivity predictions of transition metal containing catalysts, but there's less systematic work for other types of reactions. 

In this paper, Stuyver and co-workers suggest a descriptor set for barriers of hydrogen atom transfer (HAT) reactions that are based valence bond (VB) theory. In practise this translates to computing the bond dissociation energies (BDEs) without relaxing the geometry, and combining them with the BD free energies (BDFE, where ΔBDFE corresponds to ΔGrp). In addition, atomic Mulliken charges, spin densities, and buried volume are also added. All descriptors are predicted by surrogate models to avoid QM-based calculations.

Using these descriptors they get significantly better barrier predictions compared to fingerprint or graph convolution representation, even using simple models such as linear regression. Even the simple Bell-Evans-Polanyi model (a linear model based solely on ΔGrp) outperforms the models using fingerprints and graph convolution, with an R2 of 0.71 compared to 0.65 for graph convolution. For, comparison the R2s for the VB-based descriptors are 0.80-0.85, depending on the ML-model.

I wonder what other approximate chemical methods contain inspirations for new descriptors?


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