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Active learning facilitated adsorption predictions in porous materials by incorporating “alchemical” molecules, fugacity, and material features (PC1, PC2) using Gaussian process regression. 

Metal–organic frameworks (MOFs) are promising materials with various applications, and machine learning (ML) techniques can enable their design and understanding of structure–property relationships. In this paper, we use machine learning (ML) to cluster the MOFs using two different approaches. For the first set of clusters, we decompose the data using the textural properties and cluster the resulting components. We separately cluster the MOF space with respect to their topology. The feature data from each of the clusters were then fed into separate neural networks (NNs) for direct learning on an adsorption task (methane or hydrogen). The resulting NNs were then used in transfer learning (TL) where only the last NN layer was retrained. The results show significant differences in TL performance based on which cluster is chosen for direct learning. We find TL performance depends on the Euclidean distance in the decomposed feature space between the clusters involved in the direct and TL. Similar results were found when TL was performed simultaneously across both types of clusters and adsorption tasks. We note that methane adsorption was a better source task than hydrogen adsorption. Overall, the approach was able to identify MOFs with the most transferable information, leading to valuable insights and a more comprehensive understanding of the MOF landscape. This highlights the method's potential to generate a deeper understanding of complex systems and provides an opportunity for its application in alternative datasets.