Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks

ArXi:2407.09514v3 Announce Type: replace-cross Recently, metal-organic frameworks (MOFs) have nstrated their potential as solid-state electrolytes in proton exchange membrane fuel cells. However, the number of MOFs reported to exhibit proton conductivity remains limited, and the mechanisms underlying this phenomenon are not fully elucidated, complicating the design of proton-conductive MOFs. In response, we developed a comprehensive database of proton-conductive MOFs and applied machine learning techniques to predict their proton conductivity.