Issue 43, 2020

Data enhanced Hammett-equation: reaction barriers in chemical space


It is intriguing how the Hammett equation enables control of chemical reactivity throughout chemical space by separating the effect of substituents from chemical process variables, such as reaction mechanism, solvent, or temperature. We generalize Hammett's original approach to predict potential energies of activation in non aromatic molecular scaffolds with multiple substituents. We use global regression to optimize Hammett parameters ρ and σ in two experimental datasets (rate constants for benzylbromides reacting with thiols and ammonium salt decomposition), as well as in a synthetic dataset consisting of computational activation energies of ∼2400 SN2 reactions, with various nucleophiles and leaving groups (–H, –F, –Cl, –Br) and functional groups (–H, –NO2, –CN, –NH3, –CH3). Individual substituents contribute additively to molecular σ with a unique regression term, which quantifies the inductive effect. The position dependence of substituents can be modeled by a distance decaying factor for SN2. Use of the Hammett equation as a base-line model for Δ-machine learning models of the activation energy in chemical space results in substantially improved learning curves reaching low prediction errors for small training sets.

Graphical abstract: Data enhanced Hammett-equation: reaction barriers in chemical space

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Article information

Article type
Edge Article
03 Aug 2020
02 Oct 2020
First published
02 Oct 2020
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2020,11, 11859-11868

Data enhanced Hammett-equation: reaction barriers in chemical space

M. Bragato, G. F. von Rudorff and O. A. von Lilienfeld, Chem. Sci., 2020, 11, 11859 DOI: 10.1039/D0SC04235H

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