2021
DOI: 10.1021/acs.jctc.1c01134
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Correction to Machine Learning to Predict Diels–Alder Reaction Barriers from the Reactant State Electron Density

Abstract: Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactions are so seminal in chemistry, that countless variants, with or without catalysts, have been studied and their barriers have been computed or measured experimentally. This wealth of data represents a perfect opportunity to leverage machine learning models, which could quickly predict barriers without explicit calculations or measurement. Here, we show that the topological descriptors of the quantum mechanical c… Show more

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