Data-Driven Design of Metal-Organic Frameworks for Photoelectrochemical Reactions

Abstract Metal-organic frameworks (MOFs) are modular materials that are chemically versatile, offering vast design possibilities through the combination of metal clusters, organic ligands, and framework topologies. Traditionally utilized in gas storage and separation, the high porosity and tunable properties of MOFs position them as attractive candidates for (photo)electrochemical applications, including batteries, fuel cells, and solar-driven fuel conversion. We present a data-driven workflow integrating hybrid quantum chemical calculations with a multimodal AI model (MOFTransformer) to efficiently predict oxidation and reduction potentials for over 270,000 known and hypothetical MOFs. Through high-throughput screening, we identify MOFs with band-edge alignments that are thermodynamically compatible with coupled CO2-to-CO reduction and oxygen evolution, highlighting structural motifs and electronic properties for further investigation with significantly reduced computational effort compared to traditional approaches.