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The Joint Automated Repository for Various Integrated Simulations (JARVIS) isan integrated infrastructure to accelerate materials discovery and design usingdensity functional theory (DFT), classical force-fields (FF), and machinelearning (ML) techniques. JARVIS is motivated by the Materials GenomeInitiative (MGI) principles of developing open-access databases and tools toreduce the cost and development time of materials discovery, optimization, anddeployment. 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 millioncalculated properties in JARVIS-DFT, 1,500 materials and 110 force-fields inJARVIS-FF, and 25 ML models for material-property predictions in JARVIS-ML, allof which are continuously expanding. JARVIS-Tools provides scripts andworkflows for running and analyzing various simulations. We compare ourcomputational data to experiments or high-fidelity computational methodswherever applicable to evaluate error/uncertainty in predictions. In additionto the existing workflows, the infrastructure can support a wide variety ofother technologically important applications as part of the data-drivenmaterials design paradigm. The JARVIS datasets and tools are publicly availableat the website: https://jarvis.nist.gov .

The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design