The study of photochemical reactions in general and photoelectrochemical water splitting in particular, rests on understanding of such elementary effects as light absorption, energy transfer, electron transfer, radiative and nonradiative relaxation, and catalysis is important for the rational design of efficient systems for energy conversion. The design of most efficient catalysts is pursued by change of composition, quantum confinement, size, shape, surface functionalization, magnetic doping, and mesoscale structural arrangement providing versatile tuning of timescales of available basic mechanisms and properties of materials. This symposium presents recent experimental, computational, and machine learning synergistic advances on modeling of photophysics and photochemistry at interfaces: Experimental achievements in fabrication of efficient photocatalytic interfaces and monitoring of efficiency, quantum yield, and kinetics of reactant evolution and electronic dynamics by ultrafast spectroscopy techniques stimulate further development of more precise theoretical methods. Computational modeling allows for interpretation of available experimental trends and help in guiding further advances in design of efficient photocatalytic materials.
Cheminformatics and machine learning advances help to establish a feedback loop between computation and experiment and narrow down the number of structures with high potential for record efficiency. It is expected that the symposium will bring better understanding of photoinduced processes of light absorption, formation and breaking of charge transfer excitations, hot carrier relaxation, multiple exciton processes, coupled light-to-matter states, and redox reaction dynamics at catalytic sites, affected by lattice vibrations and solvent polarization.