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
N1 - Funding Information:
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.
Publisher Copyright:
© 2020, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.
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.
AB - 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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U2 - 10.1038/s41524-020-00440-1
DO - 10.1038/s41524-020-00440-1
M3 - Article
AN - SCOPUS:85095931604
SN - 2057-3960
VL - 6
JO - npj Computational Materials
JF - npj Computational Materials
IS - 1
M1 - 173
ER -