This Project Grant award from the National Science Foundation (NSF) Engineering program (CFDA 47.041) supports the development of an accurate and efficient method for large-scale simulations of heterogeneous electrocatalysis. The $346,593 award to the University of Texas at Austin aims to enhance the capability of machine learning force field (ML FF) approaches by incorporating features to describe the behavior of electrons under the grand canonical ensemble. This "E-GCE-FF" integrated approach will be used to study pH and cation effects in heterogeneous electrocatalysis, addressing long-standing questions about oxygen reduction and hydrogen evolution on different catalysts. The expanded ML FF code will be made open-source and can be extended to other electrochemical systems like batteries and corrosion. The project will also provide training opportunities for students in computational chemistry and data science, and integrate the research results into university course curricula. This award reflects NSF's mission to support fundamental research and technological innovations that can transform scientific fields and benefit society.
Generated 3/25/25, 3:35 AM