Daniel Wines is a researcher at NIST whose work focuses on applying first-principles methods and machine learning to study next-generation quantum materials. His research interests include:
1. Discovering and understanding novel quantum materials such as correlated two-dimensional magnets, superconductors, and defects in semiconductors
2. Accurately calculating the properties of correlated materials using many-body methods beyond density functional theory (DFT)
3. Using machine learning techniques to accelerate material property predictions
Currently, Wines is working on the CHIPS Metrology project “Multiscale Modeling and Validation of Semiconductor Materials and Devices.” The goal is to develop qualitative and quantitative models for advanced semiconductor heterostructures, including material properties and the impact of interface quality using multi-scale, multi-fidelity computational approaches.
Wines holds a PhD in Physics from the University of Maryland Baltimore County (2022), an MS in Physics from the same university (2019), and a BS in Physics from Fordham University (2017).
Source: https://www.nist.gov/people/daniel-wines
Keywords: Correlated materials, Quantum Monte Carlo, Density functional theory, Machine learning techniques, Semiconductor materials
