Towards Inverse Design of Metal-Organic Frameworks to Maximize Hydrogen Storage using Deep Learning

Metal-organic frameworks (MOFs) are a class of crystalline porous materials consisting of metal nodes and organic linkers. MOFs have applications in gas separation, gas purification, and electrolytic catalysis, among other fields. Consequently, the creation of better MOFs for these purposes represents a multibillion-dollar engineering challenge. Using machine learning can help exponentially accelerate the research and discovery of suitable MOFs for these applications. We implement Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) that utilize scaled-down voxel representations of real MOFs for the inverse design of new MOFs with maximal hydrogen adsorption. High hydrogen adsorption MOFs offer a potentially safe and efficient storage method of hydrogen gas for use in fuel cells. This could be critical to environmentally friendly transportation or UAVs industries as well useful to scientists studying materials science, machine learning, and their intersection