Interaction topology theory deciphers multiscale codes of MOF-like materials.

Authors

Dong Chen, Jian Liu, Chun-Long Chen, Guo-Wei Wei

Published in

Science advances. Volume 12. Issue 34. Pages eaee8016. Aug 21, 2026. Epub Aug 21, 2026.

Abstract

Metal-organic frameworks (MOFs) and MOF-like porous materials exhibit vast structural diversity and support critical applications in gas storage, separations, and catalysis. Predictive modeling remains difficult because their structure-property relationships are multiscale and cage-like, governed by both local chemical environments and global pore-network topology. These challenges, together with sparse and unevenly distributed labeled data, hinder generalization across material families. We develop an interaction topology theory and propose the interaction topological transformer (ITT), a data-efficient framework that captures materials information across multiple scales and levels, including structural, elemental, atomic, and pairwise-elemental organization. ITT extracts scale-aware features reflecting both compositional and relational structures in complex porous frameworks and integrates them through a transformer architecture for joint reasoning across scales. Using self-supervised pretraining on more than 0.6 million unlabeled structures followed by supervised fine-tuning, ITT achieves accurate, transferable, state-of-the-art predictions for adsorption, transport, and stability properties across 17 tasks, providing a principled and scalable strategy for learning-guided discovery in diverse MOF-like materials.

PMID:
42627888
Bibliographic data and abstract were imported from PubMed on 22 Aug 2026.

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