Hierarchical Representation Learning for Metal-Organic Frameworks

Metal-organic framework (MOF) property prediction is challenging because labeled datasets remain sparse relative to the vast chemical design space, despite substantial reuse of chemically similar building blocks across structures. We introduce a three-stage framework comprising structural segmentation (MOFSEG), representation learning (MOFENC), and property modeling (MOFMOD). MOFMOD is a hierarchical multiresolution graph neural network that represents MOFs simultaneously through atoms, linker motifs, organic linkers, and inorganic nodes. It combines periodic geometry with component representations pretrained by MOFENC and exchanges information across chemically defined resolutions. This design enables representations learned from recurring MOF components to be reused across distinct crystal structures while retaining their local and periodic environments. MOFSEG provides the structural hierarchy required by the model and successfully processes 94.8\% of structures across three MOF datasets WS24, hMOF, and QMOF. On band-gap prediction, MOFMOD compares favorably against state-of-the-art MOF-specific graph and transformer models. These results demonstrate the potential of hierarchical component-level representation learning for MOF property prediction and provide a framework for transferring chemical information across the MOF design space.