Exploring the chemical design space of metal-organic frameworks for photocatalysis.

ABSTRACT: 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

SUBMITTER: Mourino B 

PROVIDER: S-EPMC12107286 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature