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,[@nos161; @kal111] opens many interesting avenues for big-data science.** ]{}

Understanding Big Data is a challenge social and natural sciences share. The need to handle huge amounts of data, often generated by the steady increase of available computing power, has inspired rapid development in big-data science. In chemistry and material science, new research initiatives (e.g., the materials genome initiative[@kal111]) have led to the generation of large databases of materials for different applications.

We focus on nanoporous materials, such as zeolites,[@pop111] metal organic frameworks (MOFs),[@fur131] zeolitic imidizolate frameworks (ZIFs),[@par061] and porous polymer networks (PPNs).[@coo091] These materials are of interest in applications ranging from gas separation and storage, to catalysis. In each case one would like to tailor-make a material that is optimal for that particular application. The chemistry of these materials allows us to obtain an essentially unlimited number of new materials.[@wil121; @chu141; @bao151; @mar131; @mar141] Indeed, in recent years the number of published synthesized nanoporous materials has grown exponentially.[@fur131] Yet, this growth is exceeded by the number of predicted 