An alternative way to explore this chemical design space is through the inverse design of materials with desired properties . The chemical design space of MOFs involves combining metal clusters and organic linkers with topologies, which gives us a highly complex design space. This complexity is partly why only a limited number of studies focused on the inverse design of MOFs. Yao et al. developed a deepgenerative model with variational autoencoders for the inverse design of MOFs with desired properties by optimizing the latent space. However, optimizing the materials with non-convex objective functions in such a high dimensional latent space can be challenging. In addition, evolutionary algorithms have commonly been used to optimize the chemical design space to identify best-performing MOFs across various applications such as gas storage and separation applications .