Category-specific topological learning of metal-organic frameworks.

ABSTRACT: Metal-organic frameworks (MOFs) are porous, crystalline materials with high surface area, adjustable porosity, and structural tunability, making them ideal for diverse applications. However, traditional experimental and computational methods have limited scalability and interpretability, hindering effective exploration of MOF structure-property relationships. To address these challenges, we introduce, for the first time, a category-specific topological learning (CSTL), which combines algebraic topology with chemical insights for robust property prediction. The model represents MOF structures as simplicial complexes and incorporates elemental categorizations to enable balanced, interpretable machine learning study. By integrating category-specific persistent homology, CSTL captures both glo

SUBMITTER: Chen D 

PROVIDER: S-EPMC11869180 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature