Seonah Kim Associate Professor

Office:

Phone: (000) 000-0000

Website: https://kimlab.colostate.edu

Google Scholar: https://scholar.google.com/citations?user=3EbMdooAAAAJ&hl=en

Education

  • Ph. D., Chemistry, University of Florida
  • M. S., Computer Science, University of Houston

About

About

Computational Modeling of Bioenergy.

Develop computational catalyst design and apply computational tools to both enzymatic and catalytic conversion processes of sustainable chemicals and polymers from plants (biomass) for a new bio-energy infrastructure. Mechanism-driven discovery of biopolymer upgrading and material design via molecular and quantum mechanics. Machine learning approach in catalyst design, and (bio)fuel and chemical property prediction tool kit development.

Recent Publications

Prediction of gas-phase homolytic bond dissociation energies at near chemical accuracy with sub-second computational cost, Peter C. St. John†, Yanfei Guan, Yeonjoon Kim, Seonah Kim†, Robert S. Paton†, 10.26434/chemrxiv.10052048 (2019) and Nature Comm., 11, 2328 (2020)

A perspective on biomass-derived biofuels: from catalyst design principles to fuel properties, Yeonjoon Kim, Anna E. Thomas, David J. Robichaud, Kristiina Iisa, Peter C. St. John, Brian D. Etz, Gina M. Fioroni, Abhijit Dutta, Robert L. McCormick, Calvin Mukarakate†, Seonah Kim†, J. Haz. Mat., 400, 5, 123198 (2020)

Integrating Experimental and Computational Studies Unravels the Ga Species Responsible for Enhancing Alkene Production during Catalytic Upgrading of Biomass Pyrolysis Vapors over Ga/ZSM-5, Kristiina Iisa, Yeonjoon Kim, Kellene A. Orton, David J. Robichaud, Rui Katahira, Michael J. Watson, Mark R. Nimlos, Joshua A. Schaidle, Calvin Mukarakate†, and Seonah Kim†, Green Chem., 22, 2403-2418 (2020) (cover page, 2020 Green Chemistry Hot Articles)

Consideration of the Aluminum Distribution in Zeolites in Theoretical and Experimental Catalysis Research, Brandon C. Knott, Claire T. Nimlos, David J. Robichaud, Mark R. Nimlos, Seonah Kim†, Rajamani Gounder†, ACS Catal., 8, 770-784 (2018).

A quantitative model for the prediction of sooting tendency from molecular structure, Peter C. St John, Paul Kairys, Dhrubajyoti D. Das, Charles S. McEnally, Lisa D. Pfefferle, David J. Robichaud, Mark R. Nimlos, Bradley T. Zigler, Robert L. McCormick, Thomas D. Foust, Yannick J. Bomble, and Seonah Kim†, Energy & Fuels, 31 (9), 9983-9990 (2017).