Interpretable Machine Learning-Based Predictions of Methane Uptake Isotherms in Metal–Organic Frameworks

Gurnani, Rishi; Yu, Zhenzi; Kim, Chiho; Sholl, David S.; Ramprasad, Rampi

doi:10.1021/acs.chemmater.0c04729

Interpretable Machine Learning-Based Predictions of Methane Uptake Isotherms in Metal–Organic Frameworks

Journal Article · 14 May 2021 · Chemistry of Materials

DOI:https://doi.org/10.1021/acs.chemmater.0c04729· OSTI ID:1851709

^[1]; Yu, Zhenzi ^[2]; ^[1]; ^[2]; ^[1]

School of Materials Science and Engineering, Georgia Institute of Technology, 30332 Atlanta, Georgia, United States
School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, 30332 Atlanta, Georgia, United States

Not provided.

Research Organization:: Georgia Institute of Technology, Atlanta, GA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: SC0012577

OSTI ID:: 1851709

Journal Information:: Chemistry of Materials, Vol. 33, Issue 10; ISSN 0897-4756

Publisher:: American Chemical Society (ACS)

Country of Publication:: United States

Language:: English

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