From light absorption to chemical reaction, every step of a photoelectrochemical (PEC) process depends on the precise interplay of material properties. In OHPERA, computational modelling plays a critical role in accelerating this development, predicting how photoactive and catalytic materials behave, how they can be improved, and how they come together in real-world devices.
This powerful approach bridges theory and experiment, enabling smarter and faster innovations on the road to renewable fuel generation.
Understanding halide perovskite nanocrystals from the inside out
One of OHPERA’s core missions is the development of efficient, stable, and lead-free halide perovskite nanocrystals for use in solar-driven hydrogen and chemical production. But these materials, while promising, also present significant scientific challenges. Their complex electronic structures, surface defects, and sensitivity to environmental factors all affect performance.
To address this, OHPERA integrates first-principles simulations based on density functional theory to explore the optoelectronic properties, redox activity, and degradation mechanisms of these nanocrystals. Unlike large-scale materials databases that rely on simplified bulk properties, this modelling work focuses on realistic nanostructures and defect-rich surfaces, offering insights far more applicable to actual PEC operation.
By connecting these predictions with experimental characterisation data, the project establishes clear structure–property relationships that guide the formulation of more robust materials. This continuous feedback loop helps identify new synthesis targets and improve the long-term efficiency and stability of PEC devices.
Catalysts that know where to go and what to do
Modelling also plays a key role in the development of OHPERA’s catalytic layers, designed for high selectivity in hydrogen evolution at the cathode and glycerol oxidation at the anode. To fine-tune these reactions, the project investigates the atomic-level behaviour of catalytic materials using advanced simulations and machine learning tools.
For glycerol oxidation, reaction pathways are mapped out in detail to identify performance-limiting steps and define key descriptors that influence selectivity. In parallel, hydrogen evolution is re-examined beyond traditional thermodynamic descriptors, enabling the design of next-generation catalysts that combine activity with sustainability.
Here again, experimental fingerprints such as spectroscopy data and surface analysis are compared with model predictions to validate findings and refine the approach.
Predicting the whole system, not just the parts
Importantly, this modelling doesn’t stop at isolated materials. The outputs, such as kinetic parameters, degradation risks, and reaction energetics, feed into larger-scale simulations that inform PEC device design and operational strategies.
By integrating theoretical and experimental knowledge across all components of the PEC system, OHPERA can not only understand what works, but why. And use that insight to move faster toward scalable, high-performing green hydrogen technologies.