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Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen

Yihuang Xiong1*, Quinn Campbell2, Julian Fanghanel1,3, Catherine Badding4, Huaiyu Wang1, Nicole Kirchner-Hall1, Monica Theibault4, Iurii Timrov5, Jared Mondschein3, Kriti Seth3, Rebecca Katz3, Andrés Molina Villarino4, Betül Pamuk6, Megan Penrod1, Mohammed Khan1, Tiffany Rivera3, Nathan Smith7, Xavier Quintana1, Paul Orbe1, Craig Fennie6, Senorpe Asem-Hiablie8, James Young9, Todd Deutsch9, Matteo Cococcioni10, Venkatraman Gopalan1, Héctor Abruña4, Raymond Schaak3, Ismaila Dabo1,8

1 Department of Materials Science and Engineering, and Materials Research Institute, The Pennsylvania State University, University Park, PA, USA

2 Sandia National Laboratories, Albuquerque, NM, USA

3 Department of Chemistry and Materials Research Institute, The Pennsylvania State University, University Park, PA, USA

4 Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA

5 Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

6 School of Applied and Engineering Physics, Cornell University, Ithaca, NY, USA

7 Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA

8 Institutes of Energy and the Environment, The Pennsylvania State University, University Park, PA, USA

9 National Renewable Energy Laboratory, Golden, CO, USA

10 Department of Physics, University of Pavia, Pavia, Italy

* Corresponding authors emails: yihuangxiong@psu.edu
DOI10.24435/materialscloud:f4-wv [version v1]

Publication date: Oct 08, 2021

How to cite this record

Yihuang Xiong, Quinn Campbell, Julian Fanghanel, Catherine Badding, Huaiyu Wang, Nicole Kirchner-Hall, Monica Theibault, Iurii Timrov, Jared Mondschein, Kriti Seth, Rebecca Katz, Andrés Molina Villarino, Betül Pamuk, Megan Penrod, Mohammed Khan, Tiffany Rivera, Nathan Smith, Xavier Quintana, Paul Orbe, Craig Fennie, Senorpe Asem-Hiablie, James Young, Todd Deutsch, Matteo Cococcioni, Venkatraman Gopalan, Héctor Abruña, Raymond Schaak, Ismaila Dabo, Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen, Materials Cloud Archive 2021.160 (2021), doi: 10.24435/materialscloud:f4-wv.


The production of hydrogen fuels, via water splitting, is of practical relevance for meeting global energy needs and mitigating the environmental consequences of fossil-fuel-based transportation. Water photoelectrolysis has been proposed as a viable approach for generating hydrogen, provided that stable and inexpensive photocatalysts with conversion efficiencies over 10% can be discovered, synthesized at scale, and successfully deployed (Pinaud et al., Energy Environ. Sci., 2013, 6, 1983). While a number of first-principles studies have focused on the data-driven discovery of photocatalysts, in the absence of systematic experimental validation, the success rate of these predictions may be limited. We address this problem by developing a screening procedure with co-validation between experiment and theory to expedite the synthesis, characterization, and testing of the computationally predicted, most desirable materials. Starting with 70150 compounds in the Materials Project database, the proposed protocol yielded 71 candidate photocatalysts, 11 of which were synthesized as single-phase materials. Experiments confirmed hydrogen generation and favorable band alignment for 6 of the 11 compounds, with the most promising ones belonging to the families of alkali and alkaline-earth indates and orthoplumbates. This study shows the accuracy of a nonempirical, Hubbard-corrected density-functional theory method to predict band gaps and band offsets at a fraction of the computational cost of hybrid functionals, and outlines an effective strategy to identify photocatalysts for solar hydrogen generation.

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File name Size Description
1.6 KiB This README.txt file contains the detailed descriptions of the contents in the metadata.
90.0 MiB This archive contains the input and output files for the following calculations: 1) vc_relax, 2) first_scfu, 3) hp, and 4) final_scf_all. The calculation details can be found in the README.txt and the included Supplementary Information.


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water-splitting photocatalysis density-functional theory DFT+U high-throughput screening SNSF MARVEL H2020

Version history:

2021.160 (version v1) [This version] Oct 08, 2021 DOI10.24435/materialscloud:f4-wv