AI-Guided Design and Property Prediction for Zeolites and Nanoporous Materials

10 Porous Molecular MaterialsExploring Structure and Property Space with Software and Artificial Intelligence

Steven Bennett and Kim E. Jelfs

Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, London, United Kingdom

10.1 Introduction

Porous molecular materials (PMMs) are a subclass of porous material, which lack the extended network formed by directional covalent or coordination bonds found in other porous materials such as metal–organic frameworks (MOFs) or covalent organic frameworks (COFs) [1]. Instead, PMMs are comprised of discrete molecular units held together by weaker non-covalent interactions. Recent developments in the field of PMMs have resulted in the discovery of several new classes of porous material [2], which offer great promise in the fields of heterogenous catalysis [3], molecular separations [4], and gas storage [5], among others [6]. Compared to their network counterparts, PMMs are rare [7], and their discovery is increasingly being contributed to by computational techniques, and, in more recent times, the outcomes of data-driven artificial intelligence (AI) methods [8]. In this chapter, we define the focus to be the computational discovery of PMMs, notably excluding porous network materials such as MOFs and zeolites. We also exclude coordination cages containing metal ions, focusing instead on their organic analogs. However, we will compare computational methods used across many material types, highlighting ...