Tag: graph network

Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries.

Sowndarya, S. S. V.; Law, J.; Tripp, C.; Duplyakin, D.; Skordilis, E.; Biagioni, D.; Paton, R. S.; St. John, P. C. ChemRxiv 2021, DOI: 10.26434/chemrxiv-2021-jm3p8

Real-time Prediction of 1H and 13C Chemical Shifts with DFT accuracy using a 3D Graph Neural Network.

Guan, Y.; Sowndarya, S. S. V.; Gallegos, L. C.; St. John, P. C.; Paton, R. S. Chem. Sci. 2021, 12, 12012-12026.

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