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

• Tác giả: Eric Ayars, Nicholas S Barnett, Robabeh Bashiri, Farid El Gabaly, Stewart He, Kwangnam Kim, Preston S Lawson, Jaydie Lee, Sadisha Nanayakkara, Monica C So, Vitalie Stavila
• Ngôn ngữ: eng
• Ký hiệu phân loại: 971.0111 *Canada
• Thông tin xuất bản: United States : Chemistry of materials : a publication of the American Chemical Society , 2025
• Bộ sưu tập: NCBI
• ID: 172428

Tóm tắt

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, [Cu