Designing green chemicals by predicting vaporization properties using explainable graph attention networks

Kim, Yeonjoon; Cho, Jaeyoung; Jung, Hojin; Meyer, Lydia E.; Fioroni, Gina M.; Stubbs, Christopher D.; Jeong, Keunhong; McCormick, Robert L.; St. John, Peter C.; Kim, Seonah

doi:10.1039/d4gc01994f

DOE OSTI · 2440103

Designing green chemicals by predicting vaporization properties using explainable graph attention networks

Kim, Yeonjoon·Cho, Jaeyoung·Jung, Hojin·Meyer, Lydia E.·Fioroni, Gina M.·Stubbs, Christopher D.·Jeong, Keunhong·McCormick, Robert L.·St. John, Peter C.·Kim, Seonah

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Abstract

Computational predictions of vaporization properties aid the de novo design of green chemicals, including clean alternative fuels, working fluids for efficient thermal energy recovery, and polymers that are easily degradable and recyclable.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY 97 MATHEMATICS AND COMPUTING critical temperature flash point graph attention networks heat of vaporization machine learning

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Kim, Yeonjoon, Cho, Jaeyoung, Jung, Hojin, Meyer, Lydia E., Fioroni, Gina M., Stubbs, Christopher D., Jeong, Keunhong, McCormick, Robert L., St. John, Peter C., Kim, Seonah. 2024-09-30. Designing green chemicals by predicting vaporization properties using explainable graph attention networks. https://doi.org/10.1039/d4gc01994f

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