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Title: Quantifying similarity of pore-geometry in nanoporous materials

Abstract

In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance in applications like carbon capture or methane storage by orders of magnitude by only modifying the pore structure. For these applications it is therefore important to identify the optimal pore geometry and use this information to find similar materials. But, the mathematical language and tools to identify materials with similar pore structures, but different composition, has been lacking. We develop a pore recognition approach to quantify similarity of pore structures and classify them using topological data analysis. This then allows us to identify materials with similar pore geometries, and to screen for materials that are similar to given top-performing structures. Using methane storage as a case study, we also show that materials can be divided into topologically distinct classes requiring different optimization strategies.

Authors:
 [1];  [2];  [3];  [2];  [4];  [1]
  1. Ecole Polytechnique Federale Lausanne (Switzlerland). Inst. of Chemical Sciences and Engineering; Univ. of California, Berkeley, CA (United States). Dept. of Chemical and Biomolecular Engineering
  2. Ecole Polytechnique Federale Lausanne (Switzlerland). Inst. of Chemical Sciences and Engineering
  3. DataShape Group, Palaiseau (France)
  4. Ecole Polytechnique Federale Lausanne (Switzlerland)
Publication Date:
Research Org.:
Univ. of California, Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1389557
Grant/Contract Number:  
SC0001015
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 8; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; applied mathematics; metal-organic frameworks; structural properties; theory and computation

Citation Formats

Lee, Yongjin, Barthel, Senja D., Dłotko, Paweł, Moosavi, S. Mohamad, Hess, Kathryn, and Smit, Berend. Quantifying similarity of pore-geometry in nanoporous materials. United States: N. p., 2017. Web. doi:10.1038/ncomms15396.
Lee, Yongjin, Barthel, Senja D., Dłotko, Paweł, Moosavi, S. Mohamad, Hess, Kathryn, & Smit, Berend. Quantifying similarity of pore-geometry in nanoporous materials. United States. doi:10.1038/ncomms15396.
Lee, Yongjin, Barthel, Senja D., Dłotko, Paweł, Moosavi, S. Mohamad, Hess, Kathryn, and Smit, Berend. Tue . "Quantifying similarity of pore-geometry in nanoporous materials". United States. doi:10.1038/ncomms15396. https://www.osti.gov/servlets/purl/1389557.
@article{osti_1389557,
title = {Quantifying similarity of pore-geometry in nanoporous materials},
author = {Lee, Yongjin and Barthel, Senja D. and Dłotko, Paweł and Moosavi, S. Mohamad and Hess, Kathryn and Smit, Berend},
abstractNote = {In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance in applications like carbon capture or methane storage by orders of magnitude by only modifying the pore structure. For these applications it is therefore important to identify the optimal pore geometry and use this information to find similar materials. But, the mathematical language and tools to identify materials with similar pore structures, but different composition, has been lacking. We develop a pore recognition approach to quantify similarity of pore structures and classify them using topological data analysis. This then allows us to identify materials with similar pore geometries, and to screen for materials that are similar to given top-performing structures. Using methane storage as a case study, we also show that materials can be divided into topologically distinct classes requiring different optimization strategies.},
doi = {10.1038/ncomms15396},
journal = {Nature Communications},
number = ,
volume = 8,
place = {United States},
year = {Tue May 23 00:00:00 EDT 2017},
month = {Tue May 23 00:00:00 EDT 2017}
}

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Cited by: 7 works
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