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. Nat. Mach. Intell. 2022, 7, 720–730
- Nature Reviews in Chemistry: Game changer for batteries
- Nature Machine Intelligence: Reinforcement learning supercharges redox flow batteries
- Tech Xplore: A molecular optimization framework to identify promising organic radicals for aqueous redox flow batteries
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
Paton Research Group
Department of Chemistry
Colorado State University
1301 Center Avenue
Ft. Collins, CO 80523-1872
patonlab@colostate.edu