Data enhanced Hammett-equation: reaction barriers in chemical space

Author(s)
Marco Bragato, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Abstract

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 rho and sigma 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 similar to 2400 S(N)2 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 sigma with a unique regression term, which quantifies the inductive effect. The position dependence of substituents can be modeled by a distance decaying factor for S(N)2. Use of the Hammett equation as a base-line model for Delta-machine learning models of the activation energy in chemical space results in substantially improved learning curves reaching low prediction errors for small training sets.

Organisation(s)
Computational Materials Physics
External organisation(s)
Universität Basel
Journal
Chemical Science
Volume
11
Pages
11859-11868
No. of pages
10
ISSN
2041-6520
DOI
https://doi.org/10.1039/d0sc04235h
Publication date
11-2020
Peer reviewed
Yes
Austrian Fields of Science 2012
104017 Physical chemistry, 102019 Machine learning
Keywords
Portal url
https://ucris.univie.ac.at/portal/en/publications/data-enhanced-hammettequation-reaction-barriers-in-chemical-space(9036867c-2244-4dd4-8f06-976e12df4e7c).html