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

Kavli Affiliate: Hector D. Abruna

| First 5 Authors: Yihuang Xiong, Quinn T. Campbell, Julian Fanghanel, Catherine K. Badding, Huaiyu Wang

| Summary:

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.

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