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The Joint Automated Repository for Various Integrated Simulations (JARVIS)infrastructure at the National Institute of Standards and Technology (NIST) isa large-scale collection of curated datasets and tools with more than 80000materials and millions of properties. JARVIS uses a combination of electronicstructure, artificial intelligence (AI), advanced computation and experimentalmethods to accelerate materials design. Here we report some of the new featuresthat were recently included in the infrastructure such as: 1) doubling thenumber of materials in the database since its first release, 2) including moreaccurate electronic structure methods such as Quantum Monte Carlo, 3) includinggraph neural network-based materials design, 4) development of unifiedforce-field, 5) development of a universal tight-binding model, 6) addition ofcomputer-vision tools for advanced microscopy applications, 7) development of anatural language processing tool for text-generation and analysis, 8) debutinga large-scale benchmarking endeavor, 9) including quantum computing algorithmsfor solids, 10) integrating several experimental datasets and 11) stagingseveral community engagement and outreach events. New classes of materials,properties, and workflows added to the database include superconductors,two-dimensional (2D) magnets, magnetic topological materials, metal-organicframeworks, defects, and interface systems. The rich and reliable datasets,tools, documentation, and tutorials make JARVIS a unique platform for modernmaterials design. JARVIS ensures openness of data and tools to enhancereproducibility and transparency and to promote a healthy and collaborativescientific environment.

Recent progress in the JARVIS infrastructure for next-generation data-driven materials design