Green hydrogen is expected to play a central role in the transition to clean energy, but producing it efficiently requires the discovery of better catalysts. To help speed up this search, a team of researchers has introduced an innovative predictive framework that brings together for the first time catalyst science and Quantitative Structure-Activity Relationship ((Q)SAR) methodologies, a powerful approach widely used in fields such as toxicology, environmental science, and drug development.
Drawing on experimental data reported in the scientific literature and advanced computational modelling, the team led by Dr. Chiara Biz (MagnetoCat SL, Spain) and Dr. Mauro Fianchini (University of Alicante, Spain) developed predictive models capable of estimating the experimental overpotential, a key indicator of catalyst efficiency. Using the oxygen evolution reaction as a model system, the models can reliably predict the performance of materials that have not yet been fully tested experimentally. This innovative capability could help researchers identify the most promising candidates at an early stage, significantly reducing the need for time-consuming and costly laboratory testing.
Significantly, the proposed predictive framework also complies with internationally recognised OECD guidelines for model validation, increasing the transparency, reliability, and policy relevance of its predictions. While further efforts are underway to expand and refine the models, the study demonstrates how robust, reliable, and policy-aligned predictive tools can support the digitalisation of catalysis and speed up the development of next-generation clean energy technologies.
As demand for sustainable energy solutions grows, bringing proven (Q)SAR strategies and internationally recognised model validation guidelines into materials science could help fast-track the discovery of next-generation catalysts and support a more affordable green hydrogen economy.