  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, 1,500 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 .
