Computational design and discovery of Porous materials in the Cambridge structural database

Metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) are now an important class of nanoporous materials that have been extensively researched for use in applications that take advantage of their porosity and adsorption properties.This thesis focuses on three different applications of MOFs and COFs: CO$_2$ adsorption, catalysis, and conductivity. Three different yet complementary approaches were used to identify promising materials. The first approach is the structure prediction of new materials by combining simulations and experiments. Computational techniques linking classical methods and Density Functional Theory (DFT) have effectively predicted the adsorption characteristics of several MOF and COF structures, which have then been exploited for various purposes, e.g. CO$_2$ capture and catalysis. The second approach involves high-throughput screening of a database of previously synthesised MOFs. This screening was executed by utilising DFT techniques in order to calculate the materials band gap and electronic properties thus identifying conductive MOFs. The third strategy, employed to develop novel conductive MOFs, is computer-aided the design of new, hypothetical MOFs prior to experimental testing. The combination of these approaches enables us to investigate the characteristics of materials in depth and maximises the predictive power of computational analysis.