Fueling up hydrogen production

Through machine learning, an LLNL scientist has a better grasp of understanding materials used to produce hydrogen fuel. The interaction of water with TiO₂ (titanium oxide) surfaces is especially important in various scientific fields and applications, from photocatalysis for hydrogen production to photooxidation of organic pollutants to self-cleaning surfaces and biomedical devices. However, the surface chemistry of TiO₂ in contact with water has been the subject of several debates among scientists. The challenge is: If one really wants to understand how water is converted to H₂ and O₂ by TiO₂, one must first understand the chemistry of the region where this reaction takes place. In a paper appearing in the Proceedings of the National Academy of Sciences, LLNL researchers used molecular simulations that accurately reproduce the ab initio results for water interacting with a prototypical TiO₂(110) surface. The results have implications for improved production of hydrogen fuel. “Our fundamental understanding of the TiO₂ interface with water will help us to find more efficient and affordable materials to produce hydrogen in a clean and renewable fashion,” said Marcos Calegari Andrade, materials scientist in the Quantum Simulations Group and co-author of the paper. The work highlights how powerful machine learning is to study chemical reactions at solid-liquid interfaces (such as the TiO₂-water interface). Machine learning allows for long and large-scale atomistic simulations with the accuracy of quantum mechanics but at orders of magnitude lower computational costs. Read more at LLNL News.