Why Do We Use the Materials and Operating Conditions We Use for Heterogeneous (Photo)Electrochemical Water Splitting?

Water splitting through the use of (photo)electrocatalysts is a carbon-free route to sustainably produce hydrogen gas for use in various applications, such as ammonia and methanol synthesis, hydrotreating of petroleum and biomass, and clean electricity production in fuel cells. Over the past few decades, many candidate materials (e.g., metals and their oxides, nitrides, sulfides, and phosphides) in various 2D and 3D architectures and structures (e.g., layered double hydroxides and oxyhydroxides, rutiles, spinels, and perovskites) have been evaluated for catalyzing the two half-reactions involved in water splitting: hydrogen and oxygen evolution. Moreover, these materials are used under varied operating conditions, such as low and high temperatures, pressures, and solution pH, and in the presence or absence of sunlight. Accordingly, it is valuable to examine why the materials and process conditions currently in use were explored in the first place and why some of them are more promising than others. Given the difficulties associated with experimentally characterizing electrode-electrolyte interfaces in situ, theory can be a valuable partner for gaining insight into intermediate structures, reaction mechanisms, and ultimately catalyst design. Here, we focus on how theoretical studies of crucial materials properties, such as optoelectronic properties (e.g., band gaps, band edge positions, and the kinetics of charge-carrier separation and transport), adsorption and reaction energetics, and (photo)electrochemical stability, inform the success of existing and yet-to-be-discovered (photo)electrocatalysts. We also highlight other research directions important for theoretical (photo)electrocatalyst discovery, such as the quantification of reaction kinetics versus thermodynamics, investigation of the influence of defects and surface reconstructions on (photo)electrocatalytic materials properties, and the use of data science strategies (e.g., high-throughput simulations, statistical regression, and machine learning) to accelerate the development of new materials for water splitting applications.