First principles electronic structure methods such as density functional theory (DFT) are crucial computational tools to predict materials properties at the quantum level. In addition, electronic structure methods that go beyond the accuracy of DFT such as Quantum Monte Carlo, GW, and other advanced techniques are oftentimes needed to correctly describe correlated electronic systems. In recent years, electronic structure methods have been coupled to machine learning to accelerate predictions and for multiscale modeling via machine learning force fields. This research will focus on applying a range of computational tools to realistic material systems, including interfaces and defects. This research will combine electronic structure methods, artificial intelligence, and data-driven techniques to understand and engineer novel materials for next-generation microelectronics and quantum computing applications. We are currently focused on semiconductors, quantum materials, magnetic materials, superconductors and 2D materials. We are also interested in high-throughput calculations and the creation of various materials databases to be integrated into the NIST-JARVIS (https://jarvis.nist.gov/) infrastructure. We work closely with experimental collaborators for validation and focus on releasing software, models, workflows, and data for the community. 

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