There exist numerous potential benefits to identifying organic materials which can replicate the electronic properties of inorganic systems, particularly rare-earth metals which are costly to obtain and pose environmental risks. Superconductivity and ferroelectricity are two such electronic property which has recently been identified in metal organic frameworks (MOFs). This motivates a search for functional MOFS within the organic material database (OMDB) developed by the theoretical quantum matter group at NORDITA. The OMDB is the largest database of organic materials, containing over 40000 entries. We will use density functional theory and the Quantum Espresso software package to screen the ~150 suitable MOFs within the OMDB and identify optimal candidates for replacing known inorganic compounds. We will further utilize the collected data to train generative neural networks to perform inverse design of novel functional organic compounds using diffusion networks. This project builds on past steady support for materials development in OMDB by SNIC.