Work

Interaction topology theory deciphers multiscale codes of MOF-like materials

Year: 2026

Type: article

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.

Source: Science Advances

DOI: 10.1126/sciadv.aee8016

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

Institutions: Michigan State University, Pacific Northwest National Laboratory, University of Georgia

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $4,500