Discovery of Dual Ion-Electron Conductivity of Metal-Organic Frameworks via Machine Learning-Guided Experimentation.

ABSTRACT: Identifying conductive metal-organic frameworks (MOFs) with a coupled ion-electron behavior from a vast array of existing MOFs offers a cost-effective strategy to tap into their potential in energy storage applications. This study employs classification and regression machine learning (ML) to rapidly screen the CoREMOF database and experimental methodologies to validate ML predictions. This process revealed the structure-property relationships contributing to MOFs' bulk ion-electron conductivity. Among the 60 conductive compounds predicted, only two p-type conductive MOFs, [Cu₃(μ₃-OH) (μ₃-C₄H₂N₂O₂)₃(H₃O)]·2C₂H₅OH·4H₂O (1) and NH₄[Cu₃

SUBMITTER: Bashiri R 

PROVIDER: S-EPMC11823006 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature