From Wikipedia, the free encyclopedia
Indian American physicist and chemist
Kamal Choudhary (born 1989) is an
Indian American
physicist and computational materials scientist in the thermodynamics and kinetics group at the
National Institute of Standards and Technology .
[1] He is most notable for establishing the NIST-JARVIS infrastructure
[2] for data-driven materials design and
Materials informatics . He is also an associate editor of the journals
npj Computational Materials and
Scientific Data .
Career
In 2015 Choudhary joined the Material Measurement laboratory at
National Institute of Standards and Technology . He was awarded NIST accolade award in the field of data computing and data sharing for materials design in 2017.
[3] He was a speaker at the 2022
Massachusetts Institute of Technology 's GraphEx symposium
[4] and
Lawrence Berkeley National Lab 's symposium.
[5] His research was highlighted by
Texas Advanced Computing Center .
[6]
Prior to his tenure at
NIST he was a graduate student researcher at the
University of Florida in
Susan Sinnott 's computational materials science lab.
[7] He is also the founder and CEO of a small start-up company,
DeepMaterials , which is focused on providing materials informatics and advanced computing solutions.
[8]
Choudhary's research involves the development and application of computational methods using
classical mechanics ,
quantum mechanics and
artificial intelligence techniques to understand the electronic and atomic structure of materials.
[9] In particular, he has developed the NIST-JARVIS infrastructure.
[10] His research topics include
condensed matter physics ,
density functional theory ,
force field ,
graph neural network
[11] and
quantum computation
[12] algorithm development. His research work has led to computational discovery of several classes of materials including:
Single-layer materials ,
[13]
Solar cell ,
[14]
Topological insulator ,
[15]
Superconductors ,
[16]
Thermoelectrics
[17] and
Dielectrics .
[18]
References
^ Choudhary, Kamal (29 March 2019).
"Kamal Choudhary NIST webpage" . Nist .
^
https://jarvis.nist.gov/
^ NIST, Accolade (7 September 2017).
"MML Science data management and capabilities" . Nist .
^ GraphEx, symposium.
"MIT" .
^ LBNL, symposium.
"Deep Learning and Quantum Computation Methods for materials design" .
YouTube .
^ Texas Advanced Computing Center, Facility.
"An AI Assistant for Material Discovery" .
^ Choudhary, Kamal (2016). "Computational Design Of Surfaces, Nanostructures and Optoelectronic Materials".
arXiv :
1605.08388 [
cond-mat.mtrl-sci ].
^ DeepMaterials, Materials Informatics and Advanced Computing Company.
"DeepMaterials LLC" .
^ Choudhary, Kamal.
"Google scholar publication index" .
^ Choudhary, Kamal; Garrity, Kevin F.; Reid, Andrew C. E.; DeCost, Brian; Biacchi, Adam J.; Hight Walker, Angela R.; Trautt, Zachary; Hattrick-Simpers, Jason; Kusne, A. Gilad; Centrone, Andrea; Davydov, Albert; Jiang, Jie; Pachter, Ruth; Cheon, Gowoon; Reed, Evan; Agrawal, Ankit; Qian, Xiaofeng; Sharma, Vinit; Zhuang, Houlong; Kalinin, Sergei V.; Sumpter, Bobby G.; Pilania, Ghanshyam; Acar, Pinar; Mandal, Subhasish; Haule, Kristjan; Vanderbilt, David; Rabe, Karin; Tavazza, Francesca (12 November 2020). "The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design". npj Computational Materials . 6 (1): 173.
arXiv :
2007.01831 .
Bibcode :
2020npjCM...6..173C .
doi :
10.1038/s41524-020-00440-1 .
S2CID
226303520 .
^ Choudhary, Kamal; DeCost, Brian (15 November 2021). "Atomistic Line Graph Neural Network for improved materials property predictions". npj Computational Materials . 7 (1): 185.
arXiv :
2106.01829 .
Bibcode :
2021npjCM...7..185C .
doi :
10.1038/s41524-021-00650-1 .
S2CID
235313398 .
^ Choudhary, Kamal (22 September 2021). "Quantum computation for predicting electron and phonon properties of solids". Journal of Physics: Condensed Matter . 33 (38): 385501.
arXiv :
2102.11452 .
Bibcode :
2021JPCM...33L5501C .
doi :
10.1088/1361-648X/ac1154 .
PMID
34225258 .
S2CID
235744804 .
^ Choudhary, Kamal; Kalish, Irina; Beams, Ryan; Tavazza, Francesca (12 July 2017).
"High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory" . Scientific Reports . 7 (1): 5179.
Bibcode :
2017NatSR...7.5179C .
doi :
10.1038/s41598-017-05402-0 .
PMC
5507937 .
PMID
28701780 .
^ Choudhary, Kamal; Bercx, Marnik; Jiang, Jie; Pachter, Ruth; Lamoen, Dirk; Tavazza, Francesca (13 August 2019).
"Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods" . Chemistry of Materials . 31 (15): 5900–5908.
doi :
10.1021/acs.chemmater.9b02166 .
PMC
7067045 .
PMID
32165788 .
^ Choudhary, Kamal; Garrity, Kevin F.; Tavazza, Francesca (12 June 2019).
"High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage" . Scientific Reports . 9 (1): 8534.
arXiv :
1810.10640 .
Bibcode :
2019NatSR...9.8534C .
doi :
10.1038/s41598-019-45028-y .
PMC
6561936 .
PMID
31189899 .
S2CID
119328972 .
^ Choudhary, Kamal; Garrity, Kevin (22 November 2022). "Designing high-TC superconductors with BCS-inspired screening, density functional theory, and deep-learning". npj Computational Materials . 8 (1): 244.
arXiv :
2205.00060 .
Bibcode :
2022npjCM...8..244C .
doi :
10.1038/s41524-022-00933-1 .
S2CID
248495908 .
^ Choudhary, Kamal; Garrity, Kevin F; Tavazza, Francesca (11 November 2020). "Data-driven discovery of 3D and 2D thermoelectric materials". Journal of Physics: Condensed Matter . 32 (47): 475501.
arXiv :
1906.06024 .
Bibcode :
2020JPCM...32.5501C .
doi :
10.1088/1361-648X/aba06b .
PMID
32590376 .
S2CID
189898295 .
^ Choudhary, Kamal; Garrity, Kevin F.; Sharma, Vinit; Biacchi, Adam J.; Hight Walker, Angela R.; Tavazza, Francesca (27 May 2020). "High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses". npj Computational Materials . 6 (1): 64.
arXiv :
1910.01183 .
Bibcode :
2020npjCM...6...64C .
doi :
10.1038/s41524-020-0337-2 .
S2CID
203641719 .
External links
International National Academics