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 - 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 -