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Kamal Choudhary

Research Projects:

  • JARVIS (Joint Automated Repository for Various Integrated Simulations) home-page: https://jarvis.nist.gov/
  • JARVIS-FF: High-throughput evaluation of force-fields used in classical molecular dynamics simulation. Link: http://www.ctcms.nist.gov/~knc6/periodic.html
  • 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.

Publications

Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics

Author(s)
Kamal Choudhary, Aaron G. Kusne, Francesca M. Tavazza, Jason R. Hattrick-Simpers, Rama K. Vasudevan, Apurva Mehta, Ryan Smith, Lukas Vlcek, Sergei V. Kalinin, Maxim Ziatdinov
The use of advanced data analytics, statistical and machine learning approaches (‘AI’) to materials science has experienced a renaissance, driven by advances in
Created March 29, 2019