  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
(1,2). 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. Here we develop a pore
recognition approach to quantify similarity of pore structures and classify
them using topological data analysis (3,4). Our approach 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 -- and that each class requires different optimization strategies. In
this work we have focused on pore space, but our topological approach can be
generalised to quantify similarity of any geometric object, which, given the
many different Materials Genomics initiatives (5,6), opens many interesting
avenues for big-data science.
