Solar energy is the most abundant energy source available to humankind, but this energy cannot be harnessed on demand due to the variability of sunlight. Artificial photosynthesis overcomes that variability through the direct photocatalytic storage of solar power into chemical fuels. Nevertheless, most of the stable photocatalysts in use today rely on metal oxide semiconductors whose bandgap does not match the solar spectrum. This presentation will discuss the development and experimental validation of a widely applicable computational protocol to understand, predict, and optimize visible-light-active materials that can split water into hydrogen and oxygen with a focus on answering the critical questions that surround (1) solar compatibility using electronic-structure methods beyond density-functional theory, (2) electrochemical stability by exploiting quantum-continuum embedding methods, and (3) band-edge alignment by means of machine-learning statistical techniques.