  We present a highly efficient workflow for designing semiconductor structures
with specific physical properties, which can be utilized for a range of
applications, including photocatalytic water splitting. Our algorithm generates
candidate structures composed of earth-abundant elements that exhibit optimal
light-trapping, high efficiency in \ce{H2} and/or \ce{O2} production, and
resistance to reduction and oxidation in aqueous media. To achieve this, we use
an ionic translation model trained on the Inorganic Crystal Structure Database
(ICSD) to predict over thirty thousand undiscovered semiconductor compositions.
These predictions are then screened for redox stability under Hydrogen
Evolution Reaction (HER) or Oxygen Evolution Reaction (OER) conditions before
generating thermodynamically stable crystal structures and calculating accurate
band gap values for the compounds. Our approach results in the identification
of dozens of promising semiconductor candidates with ideal properties for
artificial photosynthesis, offering a significant advancement toward the
conversion of sunlight into chemical fuels.
