The Paton Research Group

Computational Organic Chemistry

Research in the Paton group is focused on the development and application of computational tools to accelerate chemical discovery. Quantum chemistry, open source software and statistical modeling tools are used to explore organic reactivity and selectivity aided by extensive collaborations with experimentalists.

Research

Data-Driven Chemistry

Catalyst Discovery and Design

Reaction Mechanism and Selectivity

Lab Photos

Recent Publications

Deep learning for asymmetric catalysis.

Paton, R. S.; Kim, S. Nat. Comput. Sci. 2026, accepted

The Atroposelective Iodination of 2-Amino-6-arylpyridines Catalyzed by Chiral Disulfonimides Actually Proceeds via Brønsted Base Catalysis: A Combined Experimental, Computational, and Machine-Learning Study.

Parmar, K. S.; Bawel, S.; Popescu, M. V.; Mai, B. K.; Timmerman, J. C.; Altundas, B.; Paton, R. S.; Denmark, S. E. J. Am. Chem. Soc. 2026, accepted

Direct Aziridine Synthesis from Amines and Styrenes via a Base-Promoted Oxidative Cascade Reaction.

Vásquez Tapia Vera, C.; Hughes, W. B.; Klaus, D. R.; Paton, R. S.; Bandar, J. S. J. Am. Chem. Soc. 2025, accepted

Operando Analysis of Nickel Catalyst Speciation in Reductive Biaryl Synthesis Using Thin Layer Electrochemistry and a Microelectrode.

Punchihewa, B. T.; Romero-Arenas, A.; Paton, R. S.; Weix, D. J.; Stahl, S. S.; Rafiee, M. J. Am. Chem. Soc. 2025, 147, 44667–44672

Direct C–H Lactonization of Carboxylic Acids Enabled by LMCT Photoactivation.

Weber, K. M.; Villanueva, R.; Popescu, M. V.; Lutovsky, G. A.; Gockel, S. N.; Paton, R. S.; Yoon, T. P. Angew. Chem. Int. Ed. 2025, e15582

A Fragment Based Approach Towards Curating, Comparing and Developing Machine Learning Models Applied in Photochemistry.

Pérez-Soto, R., Popescu, M. V.; Kumar, S.; Adao Gomes, L. Lee, C.; Shore, E.; Lopez, S. A.; Paton, R. S.; Kim, S. Chem. Sci. 2025, 16, 21874-21886