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Cited by 116Metal-Organic Frameworks: Synthesis and ApplicationsCovalent Organic Framework ApplicationsMachine Learning in Materials Science

Construction and Characterization of Structure Models of Crystalline Porous Polymers

Martin, Richard Luis, Haranczyk, Maciej·2014·Crystal Growth & Design
DOI 10.1021/cg500158c
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Abstract

Metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) are examples of advanced porous polymeric materials that have emerged in recent years. Their crystalline structure and modular synthesis offer unmatched versatility in their design. By exchanging chemical building blocks, one can both explore the unlimited space of possible structural chemistry within an isoreticular (same crystal topology) series and achieve a wide range of alternative topologies. This reticular paradigm potentially enables the design of structures with any desired porosity and internal surface chemistry. Reliable structure models are typically required in order to predict material properties using a broad spectrum of molecular modeling techniques. In this work, we introduce an algorithm for the assembly of crystalline porous polymer structure models which permits precise control over the underlying topologies of the generated models. This tool has been applied to high-throughput combinatorial structure enumeration and optimization-based automated design. Here, we demonstrate applications of this tool in crystal structure modeling tasks for both MOFs and COFs. Our algorithm is made available within our open source Zeo++ software suite.

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Crossref · 10.1021/cg500158c

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52 of 52 references verified0 citation errors · 0 not checkable

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  • Combinatorial enumeration with small changes in linker chemistry and metal SBUs can result in large data sets of 100k or more MOFs, although how well such sets represent the diversity of synthetically accessible materials has been questioned.

    “Combinatorial enumeration with small changes in linker chemistry (e.g., functional groups) and metal SBUs can result in large data sets of 100k or more MOFs (27,28,38,39), although how well such sets represent the diversity of synthetically accessible materials has been questioned (40)”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [39]

  • Demonstrations have included the screening of the more than 13k MOFs in the ToBaCCo set for hydrogen storage or other similarly large MOF sets for methane storage.

    “Demonstrations have included the screening of the more than 13k MOFs in the ToBaCCo set (28) for hydrogen storage (47) or other similarly large MOF sets for methane storage (39, 48).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [39]

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Authors
Martin, R. L., & Haranczyk, M.
Affiliations
  • Lawrence Berkeley National Laboratory (US)
  • Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720-8139, United States
Venue
Crystal Growth & Design
Publisher
American Chemical Society (ACS)
Published
2014-04-02
Volume
14
Issue
5
Pages
2431-2440
ISSN
  • 1528-7483 (ISSN-L)
  • 1528-7505
DOI
10.1021/cg500158c
OpenAlex
W2335274251
Cited by
116
References
53
Citations by year
2026: 9 · 2025: 9 · 2024: 14 · 2023: 10 · 2022: 12 · 2021: 10 · 2020: 8 · 2019: 8 · 2018: 8 · 2017: 7 · 2016: 8 · 2015: 6 · 2014: 7
FWCI
3.97
Type
journal article
Language
en
Open access
closed
Locations
Crystal Growth & Design (publishedVersion)
Topics
  • Physical Sciences › Chemistry › Inorganic Chemistry › Metal-Organic Frameworks: Synthesis and Applications
  • Physical Sciences › Materials Science › Materials Chemistry › Covalent Organic Framework Applications
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
metal-organic frameworks, covalent organic frameworks, crystal topology, crystal structure modeling

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