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Daniel Wines (Fed)
Physicist
Research Interests:
My research focuses on applying first-principles methods such as density functional theory (DFT) and Quantum Monte Carlo (QMC) in conjunction with machine learning techniques to study next-generation quantum materials. Specifically, I am interested in correlated two-dimensional magnets, superconductors, and defects in semiconductors. My work can be divided into three categories: 1) the discovery and understanding of novel quantum materials, 2) accurately calculating the properties of correlated materials using many-body methods beyond DFT, and 3) using machine learning techniques to accelerate material property predictions. Currently, I am working as part of the CHIPS Metrology project "Multiscale Modeling and Validation of Semiconductor Materials and Devices", in which our goal is to develop qualitative and quantitative models for advanced semiconductor heterostructures, including material properties and the impact of the interface quality via multi-scale, multi-fidelity computational approaches.
Current Projects:
- Beyond DFT implementation in JARVIS (https://jarvis.nist.gov/)
- Quantum Monte Carlo simulations of solids
- Computational studies of 2D magnets and superconductors
- Multiscale Modeling and Validation of Semiconductor Materials and Devices (https://www.nist.gov/programs-projects/multiscale-modeling-and-validati…)
Outreach:
- Organizer of the NIST Quantum Matters in Materials Science (QMMS) Workshop
- Organizer of the recurring NIST Artificial Intelligence in Materials Science (AIMS) Workshop
Awards
- NRC Postdoctoral Fellowship
- 2024 MML Accolade: MML Postdoctoral Fellow