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Kamal Choudhary (Assoc)

My research interests are focused on atomistic materials design using classical, quantum, and machine learning methods. In particular, I have developed JARVIS database and tools (https://jarvis.nist.gov/) that hosts publicly available datasets for millions of material properties.

Some of my research projects:

  • JARVIS-FF: High-throughput evaluation of force-fields used in classical molecular dynamics simulation. Link: https://jarvis.nist.gov/jarvisff/ 
  • JARVIS-DFT: High-throughput identification and characterization of two-dimensional, solar-cell, topological and high-performance materials using quantum density functional theory. Link: https://jarvis.nist.gov/jarvisdft/
  • JARVIS-ML: Classical force-field inspired descriptors (CFID) for materials, active learning, combining experiments with computational data. Link: https://jarvis.nist.gov/jarvisml
  • Development of classical force-fields using charge-optimized many body formalism and Neural-network.
  • Implementation of Artificial Intelligence  (AI) and quantum algorithm tools in Materials Science and Engineering.

Publications

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

Author(s)
Kamal Choudhary, Kevin F. Garrity, Andrew C. Reid, Brian DeCost, Adam J. Biacchi, Angela Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, Aaron Kusne, Andrea Centrone, Albert Davydov, Francesca Tavazza, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, David Vanderbilt, Karin Rabe
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using

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, Updated February 7, 2021