Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks

Bucior, Benjamin J.; Bobbitt, N. Scott; Islamoglu, Timur; Goswami, Subhadip; Gopalan, Arun; Yildirim, Taner; Farha, Omar K.; Bagheri, Neda; Snurr, Randall Q.

doi:10.1039/c8me00050f

Cited by 308Metal-Organic Frameworks: Synthesis and ApplicationsMachine Learning in Materials ScienceHydrogen Storage and Materials

Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks

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Bucior, Benjamin J., Bobbitt, N. Scott, Islamoglu, Timur, Goswami, Subhadip, Gopalan, Arun, Yildirim, Taner, Farha, Omar K., Bagheri, Neda +1 more·2019·Molecular Systems Design & Engineering

DOI 10.1039/c8me00050f

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Abstract

A new, energy-based descriptor for porous materials is highly predictive for hydrogen adsorption using an interpretable regression model.

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Other 3D-focused approaches include the development of maps of electrostatics with probe molecules, an approach that has recently gained favor in deep learning models for predicting gas storage characteristics of MOFs, or experimental homogeneous catalyst activities, where it is sometimes called stereocartography.
“Other 3D-focused approaches include the development of maps of electrostatics with probe molecules (71), an approach that has recently gained favor in deep learning models for predicting gas storage characteristics of MOFs (80), or experimental homogeneous catalyst activities (81), where it is sometimes called stereocartography (82).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [80]
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Authors: Bucior, B. J., Bobbitt, N. S., Islamoglu, T., Goswami, S., Gopalan, A., Yildirim, T., Farha, O. K., Bagheri, N., & Snurr, R. Q.
Affiliations: Northwestern University (US)
Department of Chemistry
National Institute of Standards and Technology (US)
Corresponding author: Bagheri, Neda
Snurr, Randall Q.
ORCID: Bucior, Benjamin J.
Bobbitt, N. Scott
Islamoglu, Timur
Goswami, Subhadip
Gopalan, Arun
Yildirim, Taner
Farha, Omar K.
Bagheri, Neda
Snurr, Randall Q.
Venue: Molecular Systems Design & Engineering
Publisher: Royal Society of Chemistry (RSC)
Published: 2019
Volume: 4
Issue: 1
Pages: 162-174
ISSN: 2058-9689 (ISSN-L)
DOI: 10.1039/c8me00050f
OpenAlex: W2899354085
Cited by: 308
References: 66
Citations by year: 2026: 33 · 2025: 49 · 2024: 46 · 2023: 47 · 2022: 38 · 2021: 39 · 2020: 38 · 2019: 17 · 2018: 1
FWCI: 8.83
Type: journal article
Language: en
Open access: green
www.osti.gov
Licence: rsc.li/journals-terms-of-use#chorus (am, 304 d)
Locations: Molecular Systems Design & Engineering (publishedVersion)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders: National Science Foundation (DGE-1324585)
Basic Energy Sciences (DE-FG02-17ER16362)
Topics: Physical Sciences › Chemistry › Inorganic Chemistry › Metal-Organic Frameworks: Synthesis and Applications
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Materials Science › Materials Chemistry › Hydrogen Storage and Materials
Keywords: metal-organic frameworks, hydrogen storage, hydrogen adsorption, porous media, machine learning descriptors, interpretable regression, materials property prediction

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Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks

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