This Project Grant award, funded by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program, supports theoretical and computational research to enhance the accuracy and efficiency of first-principles quantum mechanical simulations for understanding the electronic structure of materials. The research team at the University of California, Irvine aims to develop innovative machine learning-based approximations to the exact functional within density functional theory (DFT) simulations, with the goal of enabling more reliable materials discovery and design. The $220,991 award, effective from November 15, 2024 to October 31, 2027, will fund the compilation of a comprehensive database of solid materials, the implementation of new functionals within the established Jacob's Ladder approach, and the utilization of non-conventional descriptors to optimize the modeling of strong correlations in solid-state systems. The project underscores the importance of education and mentorship, as the research team is dedicated to preparing students for successful careers in academia and industry by equipping them with essential skills in artificial intelligence and innovative research practices.
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