Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study introduces a novel, multi-stage machine learning (ML) approach to streamline the discovery and optimization of complex multi-metallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we establish an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially new benchmark in electrocatalysts research.

Facebook and Carnegie Mellon University have teamed up to use artificial intelligence (AI) to discover new "electrocatalysts" capable of enhancing storage for electricity generated via renewable energy alternatives amid the climate crisis, according to a blog post from Facebook. Facebook AI has partnered with Carnegie Mellon University's (CMU's) department of chemical engineering to launch a new project called Open Catalyst Project -- which aims to accelerate quantum mechanical simulations on the order of 1,000 times faster, using AI. In this way, they hope to find new electrocatalysts needed to build more efficient and scalable means of storing and using renewable energy, according to Facebook AI's blog post. Electrocatalysts are useful because they can convert excess solar and wind power into other fuels -- like ethanol and hydrogen -- that are not as difficult to store. But present-day electrocatalysts are expensive and rare -- especially platinum.

Facebook and Carnegie Mellon University have announced they are trying to use artificial intelligence (AI) to find new "electrocatalysts" that can help to store electricity generated by renewable energy sources. Electrocatalysts can be used to convert excess solar and wind power into other fuels, such as hydrogen and ethanol, that are easier to store. However, today's electrocatalysts are rare and expensive, with platinum being a good example, and finding new ones hasn't been easy as there are billions of ways that elements can be combined to make them. Researchers in the catalysis community can currently test tens of thousands of potential catalysts a year but Facebook and Carniegie Mellon believe they can increase the number to millions, or even billions, of catalysts with the help of AI. The social media giant and the university on Wednesday released some of their own AI software "models" that can help to find new catalysts but they want other scientists to have a go as well.

Researchers are paving the way to total reliance on renewable energy as they study both large- and small-scale ways to replace fossil fuels. One promising avenue is converting simple chemicals into valuable ones using renewable electricity, including processes such as carbon dioxide reduction or water splitting. But to scale these processes up for widespread use, we need to discover new electrocatalysts--substances that increase the rate of an electrochemical reaction that occurs on an electrode surface. To do so, researchers at Carnegie Mellon University are looking to new methods to accelerate the discovery process: machine learning. Zack Ulissi, an assistant professor of chemical engineering (ChemE), and his group are using machine learning to guide electrocatalyst discovery.