ArticleJournal of chemical theory and computation2026
PoroNet: An Intrinsically Interpretable Pore Graph Neural Network for Resolving Pore-Level Adsorption in Metal-Organic Frameworks.
Article in Journal of chemical theory and computation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
Machine learning (ML) models have been widely used as efficient surrogates to predict adsorption in metal-organic frameworks (MOFs) for gas storage, chemical separations, and catalysis applications. The "black box" nature of these ML models, however, remains a significant barrier between predictions and the design of novel MOFs. In this work, we introduce PoroNet, an intrinsically interpretable graph neural network architecture built on a graph representation of the pore network (i.e., pore graph). In a pore graph, nodes represent individual pores, and edges represent pore connections. PoroNet shows highly accurate predictions of hydrogen (H
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