High-Throughput Screening of Semiconductors for Artificial Photosynthesis with Data-Mining and First Principles Calculations

We propose an algorithm for efficient design of semiconductor structures with a selected set of physical properties. We deploy our algorithm to produce semiconductor candidates for artificial photosynthesis, i.e. photocatalytic water splitting. Our candidate structures are composed of earth-abundant elements, capable of trapping sunlight, suitable for H$_2$ and/or O$_2$ production, and stable to reduction and oxidation in aqueous media. First, we predict thousands of undiscovered semiconductors compositions using an ionic translation model trained on a large experimental database. Then, we screen the predicted semiconductors compositions for redox stability under HER or OER conditions. Finally, we generate thermodynamically stable crystal structures and calculate accurate band gap values for these compounds. Ultimately we produce dozens of promising semiconductor candidates with ideal properties for artificial photosynthesis.