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

TL;DR

JARVIS is a comprehensive, open-access materials database and toolkit that has recently expanded its datasets, incorporated advanced computational methods, and added new materials classes and AI tools to accelerate materials discovery.

Contribution

This paper reports recent enhancements to the JARVIS infrastructure, including new datasets, advanced electronic structure methods, AI tools, and community engagement initiatives, advancing data-driven materials design.

Findings

Doubled the number of materials in the database.

Integrated quantum Monte Carlo and graph neural networks.

Launched large-scale benchmarking and community outreach programs.

Abstract

The Joint Automated Repository for Various Integrated Simulations (JARVIS) infrastructure at the National Institute of Standards and Technology (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 more 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 computing algorithms for solids, 10) integrating several experimental datasets and 11) staging several 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-organic frameworks, defects, and interface systems. The rich and reliable datasets, tools, documentation, and tutorials make JARVIS a unique platform for modern materials design. JARVIS ensures openness of data and tools to enhance reproducibility and transparency and to promote a healthy and collaborative scientific environment.