Tag: machine learning
Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties.Gallegos, L. C.; Luchini, G.; St. John, P. C.; Kim, S.; Paton, R. S. Acc. Chem. Res. 2021, 54, 827–836
Prediction of homolytic bond dissociation enthalpies for organic molecules at near chemical accuracy with sub-second computational cost.St John, P.; Guan, Y.; Kim, Y.; Kim, S.; Paton, R. S. Nat. Commun. 2020, 11, 2328
Quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules.St John, P.; Guan, Y.; Kim, Y.; Etz, B. D.; Kim, S.; Paton, R. S. Scientific Data 2020, 7, 244
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