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In Silico Design of Porous Polymer Networks: High Throughput Screening for Methane Storage Materials

Richard L. Martin1, Cory M. Simon2, Berend Smit2*, Maciej Haranczyk1*

1 Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

2 Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA

* Corresponding authors emails: Berend-Smit@berkeley.edu, mharanczyk@lbl.gov
DOI10.24435/materialscloud:2018.0008/v1 [version v1]

Publication date: May 15, 2018

How to cite this record

Richard L. Martin, Cory M. Simon, Berend Smit, Maciej Haranczyk, In Silico Design of Porous Polymer Networks: High Throughput Screening for Methane Storage Materials, Materials Cloud Archive 2018.0008/v1 (2018), doi: 10.24435/materialscloud:2018.0008/v1.


Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal–organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure–property relationships that govern methane storage performance were identified. The relationships are translated into experimental guidelines to realize the ideal PPN structure. We found that cooperative methane–methane attractions were present in all of the best-performing materials, highlighting the importance of guest interaction in the design of optimal materials for methane storage.

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55.2 MiB PPN Structures assembled in silico (CIF format)


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3D three-dimensional database high-throughput porous polymer networks PPN nanoporous methane storage deliverable capacities DC grand canonical Monte Carlo GCMC

Version history:

2018.0008/v1 (version v1) [This version] May 15, 2018 DOI10.24435/materialscloud:2018.0008/v1