Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen. Issue 4 (30th March 2021) - Xiong, Yihuang; Campbell, Quinn T.; Fanghanel, Julian; Badding, Catherine K.; Wang, Huaiyu; Kirchner-Hall, Nicole E.; Theibault, Monica J.; Timrov, Iurii; Mondschein, Jared S.; Seth, Kriti; Katz, Rebecca; Villarino, Andrés Molina; et al.
Abstract : We develop and demonstrate a comprehensive data-driven screening protocol with co-validation between experiment and theory to maximize the success rate of materials discovery for photocatalytic hydrogen generation. Abstract : 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 70 150 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.