Atomic Property Weighted Radial Distribution Functions Descriptors of Metal–Organic Frameworks for the Prediction of Gas Uptake Capacity
Abstract
Metal–organic frameworks (MOFs) are porous materials with exceptional host–guest properties with huge potential for gas separation. The combinatorial design of MOFs demands the in silico screening of the nearly infinite combinations of structural building blocks using efficient computational tools. We report here a novel atomic property weighted radial distribution function (AP-RDF) descriptor tailored for large-scale Quantitative Structure–Property Relationship (QSPR) predictions of gas adsorption of MOFs. A total of ∼58,000 hypothetical MOF structures were used to calibrate correlation models of the methane, N 2, and CO 2 uptake capacities from grand-canonical Monte Carlo (GCMC) simulations. The principal component analysis (PCA) transform of the AP-RDF descriptors exhibited good discrimination of MOF inorganic SBUs, geometrical properties, and more surprisingly gas uptake capacities. While the simulated uptake capacities correlated poorly to the void fraction, surface area, and pore size, the newly introduced AP-RDF scores yielded outstanding QSPR predictions for an external test set of ∼25,000 MOFs with R 2 values in the range from 0.70 to 0.82. The accuracy of the predictions decreased at low pressures, mainly for MOFs with V 2 O 2 or Zr 6 O 8 inorganic structural building units (SBUs) and organic SBUs with fluorine substituents. The QSPR models can serve as efficient filtering tools to detecting promising high-performing candidates at the early stage of virtual high-throughput screening of novel porous materials. The predictive models of the gas uptake capacities of MOFs are available online via our MOF informatics analysis (MOFIA) tool.
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Low-cost, topological, three-dimensional, one-hot chemical encoding, and geometric descriptors are often employed for this purpose in transition-metal chemistry.
“Low-cost, topological (5, 7, 32, 92-94), three-dimensional (95-100), one-hot chemical encoding (92, 101), and geometric (e.g., for MOF pores) (102-104) descriptors are often employed for this purpose in transition-metal chemistry.”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [95]
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- Authors
- Fernandez, M., Trefiak, N. R., & Woo, T. K.
- Affiliations
- Centre for Catalysis Research and Innovation, Department of Chemistry, University of Ottawa, Ottawa, Canada
- University of Ottawa (CA)
- Corresponding author
- Woo, Tom K.
- ORCID
- Woo, Tom K.
- Venue
- The Journal of Physical Chemistry C
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-07-01
- Volume
- 117
- Issue
- 27
- Pages
- 14095-14105
- ISSN
- 1932-7447 (ISSN-L)
- 1932-7455
- OpenAlex
- W2332703416
- Cited by
- 167
- References
- 47
- Citations by year
- 2026: 12 · 2025: 22 · 2024: 23 · 2023: 24 · 2022: 15 · 2021: 18 · 2020: 15 · 2019: 8 · 2018: 8 · 2017: 6 · 2016: 7 · 2015: 4 · 2014: 5
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Physical Chemistry C (publishedVersion)
- Funders
- University of Ottawa
- Canada Research Chairs
- Natural Sciences and Engineering Research Council of Canada
- 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 › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- metal-organic frameworks, radial distribution function, gas adsorption, QSPR, virtual screening, methane adsorption, nitrogen adsorption, CO2 adsorption, grand canonical Monte Carlo simulation
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