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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 ( 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: 
  • JARVIS-DFT: High-throughput identification and characterization of two-dimensional, solar-cell, topological and high-performance materials using quantum density functional theory. Link:
  • JARVIS-ML: Classical force-field inspired descriptors (CFID) for materials, active learning, combining experiments with computational data. Link:
  • 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.


Reproducible Sorbent Materials Foundry for Carbon Capture at Scale

Austin McDannald, Howie Joress, Brian DeCost, Avery Baumann, A. Gilad Kusne, Kamal Choudhary, Taner N. Yildirim, Daniel Siderius, Winnie Wong-Ng, Andrew J. Allen, Christopher Stafford, Diana Ortiz-Montalvo
We envision an autonomous sorbent materials foundry (SMF) for rapidly evaluating materials for direct air capture of carbon dioxide ( CO2), specifically

Recent Advances and Applications of Deep Learning Methods in Materials Science

Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol WooPark, Alok Choudhary, Ankit Agrawal, Simon Billinge, Elizabeth Holm, ShyuePing Ong, Chris Wolverton
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral
Created March 29, 2019, Updated September 7, 2022