persistent barcodes and the metric in this space is $D_{TS}$. Examples of the different groups are shown in SI. The inset distinguishes the top-performing materials in the training set (white circles) among all top-performing materials. These figures were obtained with the Ayasdi Core software platform (ayasdi.com). Lens: Neighborhood lens (Resolution 30, Gain 3.0x), see Ayasdi manual. Nodes are colored by the average value of the heats of adsorption of the materials in a cluster (Red: high value, Blue: low value).

FIGURES {#figures .unnumbered}
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[Supplementary Information]{}\
 \
[Pore-geometry recognition: on the importance of quantifying similarity in nanoporous materials]{}\
Yongjin Lee, Senja D. Barthel, Paweł Dłotko, S. Mohamad Moosavi, Kathryn Hess, and Berend Smit\
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correspondence to: berend.smit@epfl.ch

 \
 \
[**This PDF file includes:**]{}

-   Text sections

    -   Description of the methods and computational details

    -   Additional similarity studies

    -   Additional screening studies (global structural properties)

    -   Description of the mathematical basis of persistent homology

    -   Detailed acknowledgment

    -   Additional tables and figures

-   Figs. SI-1 to SI-7

-   Tables SI-1 to SI-3

-   