Quantum based machine learning of competing chemical reaction profiles
Year: 2020
Type: preprint
Abstract: Kinetic and thermodynamic effects govern the outcome of competing chemical reactions, and are key in organic synthesis. They are crucially influenced, if not dominated, by the chemical composition of the reactants. For two competing exemplary reactions, E2 and SN2, we show how to use quantum machine learning in chemical compound space to rapidly predict outcome and respective transition states for any new reactant. Machine learning model based predictions of reactions in the chemical compound space of reactant candidates affords numerical results suggesting that Hammond's postulate is valid for SN2, but not to E2. The predictions are demonstrated to enable the construction of decision trees for rational prospective experimental design efforts.
Source: arXiv (Cornell University)
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3087968372