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

Kamal Choudhary; Kevin F. Garrity; Andrew C.E. Reid; Brian DeCost; Adam J. Biacchi; Angela R. Hight Walker; Zachary Trautt; Jason Hattrick-Simpers; A. Gilad Kusne; Andrea Centrone; Albert Davydov; Jie Jiang; Ruth Pachter; Gowoon Cheon; Evan Reed; Ankit Agrawal; Xiaofeng Qian; Vinit Sharma; Houlong Zhuang; Sergei V. Kalinin; Bobby G. Sumpter; Ghanshyam Pilania; Pinar Acar; Subhasish Mandal; Kristjan Haule; David Vanderbilt; Karin Rabe; Francesca Tavazza

doi:10.1038/s41524-020-00440-1

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

Kamal Choudhary, Kevin F. Garrity, Andrew C.E. Reid, Brian DeCost, Adam J. Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A. Gilad Kusne, Andrea Centrone, Albert Davydov, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei V. KalininBobby G. Sumpter, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, Kristjan Haule, David Vanderbilt, Karin Rabe, Francesca Tavazza

Center for Nanophase Matls Sciences

Research output: Contribution to journal › Article › peer-review

244 Scopus citations

Abstract

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov.

Original language	English
Article number	173
Journal	npj Computational Materials
Volume	6
Issue number	1
DOIs	https://doi.org/10.1038/s41524-020-00440-1
State	Published - Dec 2020

Funding

K.C., K.F.G., and F.T. thank the National Institute of Standards and Technology for funding, computational, and data-management resources. K.C. thanks the computational support from XSEDE computational resources under allocation number TG-DMR 190095. Contributions from K.C. were supported by the financial assistance award 70NANB19H117 from the U.S. Department of Commerce, National Institute of Standards and Technology. Contributions by S.M., K.H., K.R., and D.V. were supported by NSF DMREF Grant No. DMR-1629059 and No. DMR-1629346. X.Q. was supported by NSF Grant No. OAC-1835690. B.G.S. and S.V.K. acknowledge work performed at the Center for Nanophase Materials Sciences, a US Department of Energy Office of Science User Facility. A.A. acknowledges partial support by CHiMaD (NIST award # 70NANB19H005). G.P. was supported by the Los Alamos National Laboratory’s Laboratory Directed Research and Development (LDRD) program’s Directed Research (DR) project #20200104DR. K.C. thanks for helpful discussion with several researchers including Faical Y. Congo, Daniel Wheeler, James Warren, Carelyn Campbell, Chandler Becker, Marcus Newrock, Ursula Kattner, Kevin Brady, Lucas Hale, Eric Cockayne, Philippe Dessauw from National Institute of Standards and Technology; Karen Sauer, Igor Mazin, Nirmal Ghimire, Patrick Vora from George Mason University; Rama Vasudevan, Maxim Ziatdinov from Oak Ridge National Lab, Deyu Lu, and Matthew Carbone from Brookhaven National Lab; Marnik Bercx, Dirk Lamoen from University of Antwerp; Yifei Mo from University of Maryland; Anubhav Jain and Sinead Griffin from Lawrence Berkeley National Laboratory; Surya Kalidindi from Georgia Tech.; Tyrel McQueen and David Elbert from Johns Hopkins University; Richard Hennig from University of Florida; Giulia Galli and Ben Blaiszik from University of Chicago; Qiang Zhu from University of Nevada-Las Vegas; Dilpuneet Aidhy from University of Wyoming; Susan B. Sinnott, Tao Liang from Pennsylvania State University.

Funders	Funder number
National Science Foundation	OAC-1835690, DMR-1629346, DMR-1629059
Directorate for Computer and Information Science and Engineering	1835690
National Institute of Standards and Technology	70NANB19H005, 70NANB19H117, TG-DMR 190095
U.S. Department of Commerce
Los Alamos National Laboratory	20200104DR
Center for Hierarchical Materials Design

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10.1038/s41524-020-00440-1

Cite this

Choudhary, K., Garrity, K. F., Reid, A. C. E., DeCost, B., Biacchi, A. J., Hight Walker, A. R., Trautt, Z., Hattrick-Simpers, J., Kusne, A. G., Centrone, A., Davydov, A., Jiang, J., Pachter, R., Cheon, G., Reed, E., Agrawal, A., Qian, X., Sharma, V., Zhuang, H., ... Tavazza, F. (2020). The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design. npj Computational Materials, 6(1), Article 173. https://doi.org/10.1038/s41524-020-00440-1

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

abstract = "The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov.",

author = "Kamal Choudhary and Garrity, {Kevin F.} and Reid, {Andrew C.E.} and Brian DeCost and Biacchi, {Adam J.} and {Hight Walker}, {Angela R.} and Zachary Trautt and Jason Hattrick-Simpers and Kusne, {A. Gilad} and Andrea Centrone and Albert Davydov and Jie Jiang and Ruth Pachter and Gowoon Cheon and Evan Reed and Ankit Agrawal and Xiaofeng Qian and Vinit Sharma and Houlong Zhuang and Kalinin, {Sergei V.} and Sumpter, {Bobby G.} and Ghanshyam Pilania and Pinar Acar and Subhasish Mandal and Kristjan Haule and David Vanderbilt and Karin Rabe and Francesca Tavazza",

note = "Publisher Copyright: {\textcopyright} 2020, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.",

year = "2020",

month = dec,

doi = "10.1038/s41524-020-00440-1",

language = "English",

volume = "6",

journal = "npj Computational Materials",

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Choudhary, K, Garrity, KF, Reid, ACE, DeCost, B, Biacchi, AJ, Hight Walker, AR, Trautt, Z, Hattrick-Simpers, J, Kusne, AG, Centrone, A, Davydov, A, Jiang, J, Pachter, R, Cheon, G, Reed, E, Agrawal, A, Qian, X, Sharma, V, Zhuang, H, Kalinin, SV, Sumpter, BG, Pilania, G, Acar, P, Mandal, S, Haule, K, Vanderbilt, D, Rabe, K & Tavazza, F 2020, 'The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design', npj Computational Materials, vol. 6, no. 1, 173. https://doi.org/10.1038/s41524-020-00440-1

TY - JOUR

T1 - The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

AU - Choudhary, Kamal

AU - Garrity, Kevin F.

AU - Reid, Andrew C.E.

AU - DeCost, Brian

AU - Biacchi, Adam J.

AU - Hight Walker, Angela R.

AU - Trautt, Zachary

AU - Hattrick-Simpers, Jason

AU - Kusne, A. Gilad

AU - Centrone, Andrea

AU - Davydov, Albert

AU - Jiang, Jie

AU - Pachter, Ruth

AU - Cheon, Gowoon

AU - Reed, Evan

AU - Agrawal, Ankit

AU - Qian, Xiaofeng

AU - Sharma, Vinit

AU - Zhuang, Houlong

AU - Kalinin, Sergei V.

AU - Sumpter, Bobby G.

AU - Pilania, Ghanshyam

AU - Acar, Pinar

AU - Mandal, Subhasish

AU - Haule, Kristjan

AU - Vanderbilt, David

AU - Rabe, Karin

AU - Tavazza, Francesca

PY - 2020/12

Y1 - 2020/12

N2 - The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov.

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

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