In the field of materials informatics, featurization schemes describing materials as component vectors are common, with the aim of using ML to predict their properties. Choosing the approach transforming raw materials data into the quantitative descriptions, i.e., the features, is one of the key steps to build an accurate model. As shown in Figure (step 3), in this work, we utilise three different features to represent the metal in the MOF. First, we select 6 elemental descriptors that were consistently identified as important features in the MOF porosity prediction model developed by Pétuya et al. , with an accuracy of 80.5%, from which we obtained the ground-truth dataset. In addition, 21-dimensional elemental descriptors from magpie and 199-dimensional natural language processing (NLP)-based descriptors from mat2vec 31 are also considered. Magpie is an example of the compositionbased feature vector (CBFV) , which is a main philosophy of feature generation in MLassisted inorganic material design. It is domain-derived and represents materials based on expertly curated element properties. Moreover, it is human-readable, and potentially capable of physical interpretation. In this study, only 21 elemental descriptors are used for the metal species. Contrary to the CBFV are data-driven techniques, of which mat2vec serves as a prototype. In this method, material science knowledge is encoded as information-dense word embeddings that capture complex materials science concepts and relationships.