Tag: machine learning

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).

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).

Towards quantitative prediction of ignition-delay-time sensitivity on fuel-to-air equivalence-ratio, Richard A. Messerly, Mohammad J. Rahimi, Peter C. St. John, Jon H. Luecke, Ji-Woong Park, Nabila A. Huq, Thomas D. Foust, Tianfeng Lu, Bradley T. Zigler, Robert L. McCormick, Seonah Kim, Combustion and Flame, 214, 103-115 (2020).

Measuring and Predicting Sooting Tendencies of Oxygenates, Alkanes, Alkenes, Cycloalkanes, and Aromatics on a Unified Scale, Dhruhajyoti D. Das, Peter St. John, Charles S. McEnally, Seonah Kim, Lisa D. Pfefferle, Combustion and Flame, 190, 349-364 (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).