Exploring the chemical design space of metal–organic frameworks for photocatalysis

In this work, we introduce a combined DFT and machine learning approach to obtain insights into the chemical design of metal–organic framework (MOF) photocatalysts for hydrogen (HER) and oxygen (OER) evolution reactions. To train our machine learning models, we evaluated a dataset of 314 MOFs using a dedicated DFT workflow that computes a set of five descriptors for both closed and open shell MOFs. Our dataset is composed of a diverse selection of the QMOF database and experimentally reported MOF photocatalysts. In addition, to ensure a balanced dataset, we designed a set of MOFs (CDP–MOF) inspired by insights obtained regarding different types of photocatalytic materials. Our machine-learning approach allowed us to screen the entire QMOF and CDP–MOF databases for promising candidates. Our analysis of the chemical design space shows that we have many materials with a suitable spatial overlap of electron and hole, band gap, band-edge alignment to HER, and charge-carrier effective masses. However, we have identified in the QMOF database only a very small percentage of materials that also have the right band-edge alignment to OER. With the CDP–MOF database, we successfully targeted building blocks that potentially have the correct OER band alignment, and indeed obtained a larger percentage of materials that obey these criteria. Among those, a few motifs stood out, such as Au-pyrazolate, Ti clusters and rod-shaped metal nodes, and a particular MOF designed with the Mn₄Ca cluster, which mimics the OER center in the photosystem II of photosynthesis.