A Machine Learning Model for Automated Prediction of Bio-Oil Composition from Molecular Beam Mass Spectra

A Machine Learning Model for Automated Prediction of Bio-Oil Composition from Molecular Beam Mass Spectra. Mohammed Jabed, Yeonjoon Kim, Clark Yarbrough, Anne Harman-Ware, Jessica Olstad, Reinhard Seiser, Cheyenne Paeper, Anne Starace, Seonah Kim, ACS Sustainable Chemistry and Engineering

Designing high-performance fuels through graph neural networks for predicting cetane number of multicomponent surrogate mixtures

Designing high-performance fuels through graph neural networks for predicting cetane number of multicomponent surrogate mixtures. Yeonjoon Kim, Sabari Kumar, Jaeyoung Cho, Nimal Naser, Wonjong Ko, Peter C. St. John, Robert L. McCormick, Seonah Kim, SAE Technical Paper (Accepted 2023)

Experimental and computational studies of the production of 1,3-butadiene from 2,3-butanediol using SiO2-supported H3PO4 derivatives

Experimental and computational studies of the production of 1,3-butadiene from 2,3-butanediol using SiO2-supported H3PO4 derivatives, Juan V. Alegre-Requena, Glenn R. Hafenstine, Xiangchen Huo, Yanfei Guan, Jim Stunkel, Frederick G. Baddour, Kinga A. Unocic, Bruno C. Klein, Ryan E. Davis, Robert S. Paton, Derek R. Vardon† Seonah Kim†, Chem. Eng. J. DOI: 10.1016/j.cej.2023.143346

Designing solvent systems in chemical processes using self-evolving solubility databases and graph neural networks

Designing solvent systems in chemical processes using self-evolving solubility databases and graph neural networks. Yeonjoon Kim, Hojin Jung, Sabari Kumar, Alex Claiborne, Robert S. Paton, Seonah Kim. ChemRxiv preprint. DOI: 10.26434/chemrxiv-2022-sq34x.

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis Yeonjoon Kim, Jaeyoung Cho, Nimal Naser, Sabari Kumar, Keunhong Jeong, Robert L. McCormick, Peter C. St. John, Seonah Kim†, Proc. Comb. Inst. (accepted 2022) DOI: 10.1016/j.proci.2022.09.059

Understanding how chemical structure affects ignition-delay-time φ-sensitivity

Understanding how chemical structure affects ignition-delay-time φ-sensitivity, Richard A. Messerly, Jon H. Luecke, Peter C. St. John, Brian D. Etz, Yeonjoon Kim, Bradley T. Zigler, Robert L. McCormick, Seonah Kim†, Combustion & Flame, 225, 377-387 (2021) († denotes corresponding author)

Prediction of gas-phase homolytic bond dissociation energies at near chemical accuracy with sub-second computational cost

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 quantitative model for the prediction of sooting tendency from molecular structure

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).