Energy-based descriptors for photo-catalytically active metal-organic framework discovery

Maria Fumanal*, Gloria Capano, Senja Barthel, Berend Smit, Ivano Tavernelli

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Metal-organic frameworks (MOFs) consist of metal nodes that are connected by organic linkers. They are thus highly chemically tunable materials given the broad range of potential linkers and nodes that can be chosen for their synthesis. Their tunability has recently sparked interest in the development of new MOF photo-catalysts for energy-related applications such as hydrogen (H2) evolution and CO2 reduction. The sheer number of potentially synthesizable MOFs requires defining descriptors that allow prediction of their performance with this aim. Herein we propose a systematic computational protocol to determine two energy-based descriptors that are directly related to the performance of a MOF as a photocatalyst. These descriptors assess the UV-vis light absorption capability and the band energy alignment with respect to redox processes and/or co-catalyst energy levels. High-throughput screening based on cost-effective computations of these features is envisioned to aid the discovery of new promising photoactive systems.

Original languageEnglish
Pages (from-to)4473-4482
Number of pages10
JournalJournal of Materials Chemistry A
Issue number8
Early online date6 Feb 2020
Publication statusPublished - 28 Feb 2020


This work was supported by the EPFL and the National Centre of Competence in Research (NCCR) Materials' Revolution: Computational Design and Discovery of Novel Materials (MARVEL) of the Swiss National Science Foundation (SNSF). Calculations were performed at the EPFL High Performance Computing Center SCITAS. All the dataset les are available on the Materials Cloud Archive at https://

FundersFunder number
École Polytechnique Fédérale de Lausanne
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
National Centre of Competence in Research Robotics


    • Metal-organic frameworks
    • screening
    • electrostatic potential energy
    • Photocatalysis
    • descriptor


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