The inefficiency of current biogas synthesis methods is an unfortunate reality that has stifled biogas as a major source of energy as mankind continues the search for a viable alternative to fossil fuels. Metal-organic frameworks (MOFs) are porous membrane-like materials that hold the potential to revolutionise the process of biogas purification. However, designing these materials is a tedious task due to an infinite number of possible MOF combinations. Trying to identify the structures that have suitable properties for biogas purification using conventional lab methods would be an arduous and nearly unfeasible task. Using the foundation laid by computation chemistry screening methods, this project attempts to use machine learning to identify useful MOF structures from a much larger dataset without the need for any lab work.