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



Daniel Wines, Ramya Gurunathan, Kevin Garrity, Brian DeCost, Adam Biacchi, Francesca Tavazza, Kamal Choudhary


The Joint Automated Repository for Various Integrated Simulations (JARVIS) infrastructure at NIST is a large-scale collection of curated datasets and tools with more than 80000 materials and millions of properties. JARVIS uses a combination of electronic structure, artificial intelligence (AI), advanced computation and experimental methods to accelerate materials design. Here we report some of the new features that were recently included in the infrastructure such as: 1) doubling the number of materials in the database since its first release, 2) including accurate electronic structure methods such as Quantum Monte Carlo, 3) including graph neural network-based materials design, 4) development of unified force-field, 5) development of a universal tight-binding model, 6) addition of computer-vision tools for advanced microscopy applications, 7) development of a natural language processing tool for text-generation and analysis, 8) debuting a large scale benchmarking endeavor, 9) including quantum computation for solids, 10) integrating several experimental datasets and 11) staging several community engagement and outreach events. Several new class of materials and properties added to the database and workflows include superconductors, two-dimensional (2D) magnets, magnetic topological materials, metal-organic frameworks, defect and interface systems. The extremely rich and reliable datsets, tools, documentation, and tutorials makes JARVIS a unique platform for modern materials design. JARVIS ensures openness of data and tools to enhance reproducibility, transparency and a healthy and collaborative scientific development.
Applied Physics Reviews


Wines, D. , Gurunathan, R. , Garrity, K. , DeCost, B. , Biacchi, A. , Tavazza, F. and Choudhary, K. (2023), Recent progress in the JARVIS infrastructure for next-generation data-driven materials design, Applied Physics Reviews, [online],, (Accessed June 22, 2024)


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Created October 18, 2023, Updated October 24, 2023