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

K Choudhary and KF Garrity and ACE Reid and B DeCost and AJ Biacchi and AHR Walker and Z Trautt and J Hattrick-Simpers and AG Kusne and A Centrone and A Davydov and J Jiang and R Pachter and G Cheon and E Reed and A Agrawal and XF Qian and V Sharma and HL Zhuang and SV Kalinin and BG Sumpter and G Pilania and P Acar and S Mandal and K Haule and D Vanderbilt and K Rabe and F Tavazza, NPJ COMPUTATIONAL MATERIALS, 6, 173 (2020).

DOI: 10.1038/s41524-020-00440-1

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 approximate to 40,000 materials and approximate to 1 million calculated properties in JARVIS- DFT, approximate to 500 materials and approximate to 110 force-fields in JARVIS-FF, and approximate to 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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