Examining proton conductivity of metal-organic frameworks by means of machine learning

The tunable structure of metal–organic frameworks (MOFs) is an ideal platform to achieve contradictory requirements for proton exchange membranes, a key component of fuel cells. However, the rational design of proton-conducting MOFs remains a challenge owing to the intricate structure-property relationships that govern the target performance. In the present study, the modeling of quantities available for hundreds of MOFs is scaled up to many thousands of entities using supervised machine learning. The experimental dataset was curated to train multimodal transformer-based networks, which integrated crystal graph, energy-grid, and global-state embeddings. Uncertainty-aware models reveal superprotonic conductors among synthesized MOFs that have not been previously investigated for the considered application, highlighting magnesium-containing frameworks with aliphatic linkers as high-confidence candidates for experimental validation. Furthermore, classifiers trained on the activation energy threshold effectively discriminated between well-known proton-conduction mechanisms, providing physical insights beyond black-box routine. Our findings have proved a great potential for the data-driven materials design, which becomes a valuable addition to experimental studies devoted to proton-conducting MOFs.