Porous materials possess void spaces that can be exploited for many different applications. Depending on the specific nature of the constituent blocks, they can be further categorized into subclasses of materials including metal-organic frameworks (MOFs) , covalent organic frameworks (COFs) , porous polymer networks (PPNs) , and zeolites . Since these materials are composed of diverse combinations of molecular building blocks, the nearly infinite chemical design space presents an excellent opportunity to design these materials for a wide range of applications, including gas storage and separation , catalysis , and drug delivery . And due to the increasing number of experimental and computational structures, recently there have been several works devoted to using a data-science approach to discover and design new porous materials using various different methods. In recent years, machine learning (ML) models have shown promising results in constructing structure-property relationships for porous materials. For instance, Shi et al. have demonstrated the effectiveness of using two-dimensional (2D) energy histogram features, which include structure-gas interaction energies and energy grid gradients at grid points, as descriptors to accurately predict the gas uptake of MOFs. Also, a 3D convolutional neural network (CNN) with 3D voxel, a volume element in 3D space that is analogous to a pixel in 2D space, has been developed as a descriptor for accurate prediction of gas uptake in zeolites. For predicting electronic properties such as band gap, graph neural networks (GNNs) such as Crystal Graph Convolutional Neural Networks (CGCNN) and MatErials Graph Network (MEGNET) have shown high performance. Also, various descriptors have been developed including geometric, chemical, topological features, revised autocorrelations (RAC) and smooth overlap of atomic positions (SOAP) . Recently, MOFTransformer , a multi-modal pre-training Transformer, has been introduced to achieve universal transfer learning in MOFs, showcasing its exceptional ability to transfer learning across various MOF properties.