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

TL;DR

This paper introduces a topological data analysis method to quantify and classify pore structure similarity in nanoporous materials, aiding the discovery and optimization of materials for applications like methane storage.

Contribution

It develops a novel pore recognition approach using topological data analysis to identify and classify materials based on pore geometry, enabling better material screening.

Findings

Materials can be grouped into topologically distinct classes

Different classes require different optimization strategies

The method can be generalized to other geometric objects

Abstract

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.