  Recently, metal-organic frameworks (MOFs) have demonstrated 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. Our approach included the
construction of both descriptor-based and transformer-based models. Notably,
the transformer-based transfer learning (Freeze) model performed the best with
a mean absolute error (MAE) of 0.91, suggesting that the proton conductivity of
MOFs can be estimated within one order of magnitude using this model.
Additionally, we employed feature importance and principal component analysis
to explore the factors influencing proton conductivity. The insights gained
from our database and machine learning model are expected to facilitate the
targeted design of proton-conductive MOFs.
