An efficient method to identify and count unique structures is also presented, which enables efficient generation of machine learning descriptors containing information about chemical environment provided by functionalised linkers, which is important for the calculation of absorption properties. We propose our histograms represent useful identifiers for the pore environments in MFMs that can be used to determine the adsorption of large and complex molecules in these pore environments.