Metal-organic framework (MOFs) and related nanoporous materials have emerged as promising candidates for a variety of applications, such as gas separation and storage, catalysis, sensing, etc. Their building block structure allows us to generate a huge number of distinct materials only by changing the metal nodes and organic linkers. This, in principle, allows the design and discovery of materials that perform optimally for a given application. However, the conventional process of material development, from discovery to synthesis and performance evaluation, is too slow and expensive for exploring this enormous pool of materials. Complimentary methods are therefore needed to accelerate this process. The aim of this thesis is to investigate and expand the computational and data-driven methods for the development of nanoporous materials for gas separation and storage. The prevailing use of these methods is for high-throughput screening of the materials for a target application. However, the capability and success of such a screening approach depend on fast, reliable, and accurate prediction of material properties, as well as on the effective exploration of the chemical space. Therefore, in this thesis, we develop material descriptors for the chemistry and pore geometry of MOFs, which allow us to use machine learning to rapidly evaluate their adsorption properties with high accuracy. We next introduce a methodology to quantify the diversity of material databases to assess how well the chemical space is explored when a given material database is screened. We illustrate the importance of this diversity analysis by showing how the lack of diversity in MOF databases hinders material discovery, leads to chemical insights that are not generalisable and makes machine learning models not transferable.

The promising materials discovered in a screening study are only of interest if they can be synthesised and are sufficiently stable to withstand the operating conditions of the corresponding application. Therefore, we investigate the applicability and capability of computational and data-driven methods to address some of the challenges in the material synthesis and the mechanical stability of MOFs. Material synthesis still mainly rests on the chemical intuition of synthetic chemists. Here, we introduce a method using machine learning and a genetic algorithm to capture this chemical intuition for MOF synthesis. We demonstrate how this simple approach can be powerful for guiding the synthesis of new materials. Lastly, we study how the mechanical stability of MOFs, which is fundamentally important for most of their practical applications, is affected by its underlying structure, i.e., the framework bonding topology and ligand structure. We show how this understanding can be used to develop strategies to design MOFs with enhanced mechanical stability.