Firstly, the ML methods offer a computationally efficient alternative to high-fidelity molecular and process simulations, which allows one to expand the computational screening strategies to much larger pools of materials. The fundamental concept behind ML models is to establish a connection between adsorption patterns and certain structural or chemical characteristics of the materials. Once this connection is established, predicting new adsorption data becomes nearly instantaneous. Consequently, the primary challenge in ML model development lies in devising and computing suitable material features and training the model using an appropriate (and often very significant) array of precomputed data. Unsurprisingly, the prediction of more intricate and extensive adsorption properties necessitates a greater amount of information about the materials involved, along with more advanced characteristics. This has prompted an extensive exploration into effectively engineering and encoding these features. For instance, persistent homology, a mathematical tool for delineating the shape and connectivity of complex structures, were employed to construct advanced machine learning features. In another example, machine learning features were produced by representing the atomistic structure of materials as crystal graphs. Material affinity towards specific gas species can be incorporated using energy-based features. Recently, we proposed additional energy-based, interpretable features to improve the existing ML models for the prediction of carbon dioxide and nitrogen in the context of carbon capture. However, with the advancement of hypothetical MOF constructor by Lee et al. , it became possible to computationally generate trillions of new MOFs. Computational screening on this scale renders the detailed and advanced ML features too expensive to obtain. Thus, word embeddings, low-cost features, were introduced to accelerate the exploration of such large MOF space. Word embedding is a technique in natural language processing (NLP) and machine learning that represents words as vectors in a high-dimensional space. In the approach by Lee et al. , word embedding was introduced to represent building blocks and topologies of MOFs. The Artificial Neural Networks (ANNs) based on word embeddings as features were then applied to sift through vast numbers of MOFs to optimize Xe/Kr separation and hydrogen storage. In another example, Park et al. introduced word embeddings into a generative model aimed at constructing novel MOF structures for direct air carbon capture.