Predicting CO 2 Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks

Jian, Yue; Wang, Yuyang; Barati Farimani, Amir

doi:10.1021/acssuschemeng.2c05985

Predicting CO ₂ Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks

Journal Article · 02 December 2022 · ACS Sustainable Chemistry & Engineering

DOI:https://doi.org/10.1021/acssuschemeng.2c05985· OSTI ID:2422346

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Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States; OSTI
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States; Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States; Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States; Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States

Not provided.

Research Organization:: Carnegie Mellon Univ., Pittsburgh, PA (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

DOE Contract Number:: AR0001221

OSTI ID:: 2422346

Journal Information:: ACS Sustainable Chemistry & Engineering, Journal Name: ACS Sustainable Chemistry & Engineering Journal Issue: 50 Vol. 10; ISSN 2168-0485

Publisher:: American Chemical Society (ACS)

Country of Publication:: United States

Language:: English

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Predicting CO ₂ Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks