Quantifying similarity of pore-geometry in nanoporous materials

Yongjin Lee 1, 2 Senja Barthel 1 Paweł Dłotko 3 S. Mohamad Moosavi 1 Kathryn Hess 1 Berend Smit 1, 2
1 EPFL - Ecole Polytechnique Fédérale de Lausanne
2 Department of Chemistry [Berkeley]
3 DATASHAPE - Understanding the Shape of Data
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
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. However, 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 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.
Type de document :
Article dans une revue
Nature Communications, Nature Publishing Group, 2017, 8, pp.1-8. 〈10.1038/ncomms15396〉
Domaine :
Liste complète des métadonnées

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Yongjin Lee, Senja Barthel, Paweł Dłotko, S. Mohamad Moosavi, Kathryn Hess, et al.. Quantifying similarity of pore-geometry in nanoporous materials. Nature Communications, Nature Publishing Group, 2017, 8, pp.1-8. 〈10.1038/ncomms15396〉. 〈hal-01706966〉

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