Quantum Computational Models for Photoelectrochemical Processes in Solar Fuel and Energy Conversion Systems

Abstract

Quantum computational models are emerging as powerful tools for understanding and optimizing photoelectrochemical (PEC) processes that underpin next-generation solar fuel and energy conversion systems. These systems involve complex interactions between photons, charge carriers, catalytic surfaces, and molecular intermediates, making accurate predictive modeling challenging for classical simulation frameworks. This study investigates quantum-enabled approaches for simulating light–matter interactions, exciton dynamics, charge-transfer pathways, and catalytic reaction coordinates within PEC architectures. By leveraging quantum algorithms for electronic structure, nonadiabatic dynamics, and reaction energetics, the research explores how quantum simulations can resolve strongly correlated states, multi-electron excitations, and interfacial charge separation with higher fidelity. The analysis highlights the potential of quantum models to accelerate the discovery of efficient photoelectrode materials, enhance solar-to-fuel conversion efficiencies, and support the rational design of artificial photosynthesis systems. Finally, the paper discusses the integration of quantum computational predictions with experimental PEC workflows and outlines opportunities for future hybrid quantum–classical research.