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In most applications of nanoporous materials the pore structure is asimportant as the chemical composition as a determinant of performance. Forexample, one can alter performance in applications like carbon capture ormethane storage by orders of magnitude by only modifying the pore structure(1,2). For these applications it is therefore important to identify the optimalpore geometry and use this information to find similar materials. However, themathematical language and tools to identify materials with similar porestructures, but different composition, has been lacking. Here we develop a porerecognition approach to quantify similarity of pore structures and classifythem using topological data analysis (3,4). Our approach allows us to identifymaterials with similar pore geometries, and to screen for materials that aresimilar to given top-performing structures. Using methane storage as a casestudy, we also show that materials can be divided into topologically distinctclasses -- and that each class requires different optimization strategies. Inthis work we have focused on pore space, but our topological approach can begeneralised to quantify similarity of any geometric object, which, given themany different Materials Genomics initiatives (5,6), opens many interestingavenues for big-data science.

Pore-geometry recognition: on the importance of quantifying similarity in nanoporous materials