Previous computational studies have extensively explored the gas adsorption properties of MOFs. Molecular simulations, particularly Grand Canonical Monte Carlo (GCMC) simulations, have been widely used to predict gas adsorption behavior, such as adsorption isotherms, separation, or working capacity with high accuracy for both experimental and hypothetical databases. For experimental database screening, GCMC simulations on the earlier CoRE MOF 2014 database have been used to study the adsorption behavior of mixtures to identify MOFs with high selectivities of CO2 over H2O 61 as well as for CO2/N2 separation with pressure swing adsorption (PSA). Hypothetical databases are also frequently studied, including the study of MOFs in ToBaCCo for high H2 deliverable capacities. However, performing molecular simulations on all of the MOFs in these databases can be computationally very demanding. Even when it is straightforward to carry out GCMC simulations exhaustively, hypothetical databases often lack consideration of stability issues, with few exceptions , limiting the validity of the structure-property relationship across experimentally realizable materials. Furthermore, design rules for gas adsorption, particularly for greenhouse gases such as CH4 and CO2, are not as wellestablished as those for H2 adsorption. As a complement to simulation, machine learning (ML) models have emerged as valuable tools for screening high-performance MOFs. ML-based studies using supervised learning have been used for quantitative structure-property relations (QSPRs), prediction of mechanical stability, synthesis condition predictions, and gas adsorption studies. Descriptor selection is key in ML model training for MOF adsorption predictions, and geometric descriptors have been identified as suitable for H2 or CH4 but the same descriptors routinely fail for CO2. As an alternative, complex black-box ML models, such as deep neural networks (DNN) or a crystal graph convolutional neural network (CGCNN) have demonstrated high performance for predicting gas adsorption properties of MOFs, albeit at the cost of interpretability of the models. With recent advancements in natural language processing, transformer 79 models have increasingly been used in chemistry applications . For example, MOFormer 81 and MOFTransformer 82 have been developed to predict diverse properties including gas adsorption properties based on the string representation of MOF (MOFid) with high precision. Deep learning has also been used for screening CH4 adsorption of around 247 trillion hypothetical MOFs with 1775 topologies, as well as genetic algorithms to optimize these properties. Choosing appropriate database and feature selection are also critical in ML-based studies. Hypothetical MOF databases are valuable in the sense that they can provide large amount of training data, but the lack of similarity to experimental MOFs 42 often leads to poor transferability. That is, when ML models trained on hypothetical data are applied to a database of experimental MOFs to choose an existing MOF to deploy for a chosen task, they are likely to be error prone.