This National Science Foundation (NSF) Division of Materials Research Project Grant award supports theoretical and computational research and education to enhance the accuracy and efficiency of first-principles quantum mechanical simulations for understanding the electronic structure of materials. The $232,250 award, effective November 15, 2024 through October 31, 2027, aims to develop innovative machine learning-based approximations to the exact density functional theory functional to enable more reliable materials design. Key objectives include compiling an optimized database of solid materials, implementing new functionals, and utilizing non-conventional descriptors to model strong correlations in solid-state systems. This work will contribute to the discovery of novel materials with applications in industries such as electronics, energy, and healthcare, while also engaging students in artificial intelligence and innovative research practices. The new methodologies will be incorporated into freely available electronic structure software packages.
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