Thanks to the Materials Genome Initiative, there is now a database of millions of different classes of nanoporous materials, in particular zeolites. In this talk I will describe a computational approach to tackle high-throughput screening of this database to find the the best nano-porous materials for a given application, using a topological data analysis-based descriptor (TD) recognizing pore shapes. For methane storage and carbon capture applications, our method enables us to predict performance properties of zeolites. When some top-performing zeolites are known, TD can be used to efficiently detect other high-performing materials with high probability. We expect that this approach could easily be extended to other applications by simply adjusting one parameter: the size of the target gas molecule