There is a growing need for high-performance materials for various technological applications. To address this need, the NIST-JARVIS (https://jarvis.nist.gov/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing.
We are currently focusing on investigating semiconductors, superconductors, topological materials, solar-cells, alloys, and other classes of functional materials. We are interested in developing large scale databases with DFT, beyond-DFT, and experimental techniques. We are also interested in developing both forward and inverse machine learning models to accelerate and optimize the design processes. We work in close collaboration with NIST and outside experimentalists.
Successful candidates will have experience in density functional theory, machine learning and/or quantum techniques with some exposure to atomistic modeling for technological application. Please contact me for further information or to develop a specific project proposal.
1. "The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design" https://www.nature.com/articles/s41524-020-00440-1
Machine learning; Density functional theory; force-field; superconductors; catalysis; 2D materials; topological materials; functional materials; high-throughput; database