JARVIS-DFT: High-throughput identification and characterization of two-dimensional, solar-cell, topological and high-performance materials using quantum density functional theory. Link: http://www.ctcms.nist.gov/~knc6/JVASP.html
JARVIS-ML: Classical force-field inspired descriptors (CFID) for materials, active learning, combining experiments with computational data. Link: https://www.ctcms.nist.gov/jarvisml
Development of classical force-fields using charge-optimized many body formalism and Neural-network.
Implementation of Artificial Intelligence (AI) tools in Materials Science and Engineering.
Jason R. Hattrick-Simpers, Zachary T. Trautt, Kamal Choudhary, Aaron G. Kusne, Feng Yi, Martin L. Green, Sara Barron, Andriy Zakutayev, Nam Nguyen, Caleb Phillips, John Perkins, Ichiro Takeuchi, Apurva Mehta
High throughput experimental (HTE) techniques are an increasingly important way to accelerate the rate of materials research and development for many possible