This project aims to advance the accuracy and reliability of computer-based predictions for catalytic processes, which are vital for developing efficient and sustainable energy technologies. Leveraging state- of-the-art quantum Monte Carlo (QMC) methods and powerful supercomputing resources, the team will produce highly precise datasets to improve understanding and modeling of catalytic reactions, particularly where existing computational methods often fall short, or when the experiments are non-existent.
Efficient catalysts are essential for addressing the global energy crisis and environmental challenges by speeding up chemical reactions involved in renewable energy processes and in carbon capture technologies. However, traditional computational tools used to predict catalytic reactions often struggle to achieve the necessary accuracy, particularly for complex materials that contain transition-metal such their surfaces, single-atom catalysts, and molecular complexes. This project will utilize state-of-the-art Quantum Monte Carlo (QMC) techniques in connection with this ASCR award to advance computational methods suited to modeling such challenging catalytic systems.
By employing QMC methods and running extensive simulations on supercomputers, including Aurora and Polaris, the project will generate benchmark-quality data that surpasses current computational standards as well as resolve real experimental catalytic problems. These results will help refine existing theoretical frameworks and significantly enhance the reliability of catalyst design. Furthermore, the team will make the generated datasets publicly available at our Catalysis-hub.org, facilitating broad scientific collaboration and accelerating the development of catalysts capable of efficiently converting carbon dioxide and producing renewable fuels. This work aligns closely with the Department of Energy’s mission of fostering sustainable and clean-energy technologies.