{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/jarvis-an-integrated-infrastructure-for-data","title":"The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design","arxiv_id":"2007.01831","date":"2020-07-03","proceeding":null,"authors":["Kamal Choudhary","Kevin F. Garrity","Andrew C. E. Reid","Brian DeCost","Adam J. Biacchi","Angela R. Hight Walker","Zachary Trautt","Jason Hattrick-Simpers","A. Gilad Kusne","Andrea Centrone","Albert Davydov","Jie Jiang","Ruth Pachter","Gowoon Cheon","Evan Reed","Ankit Agrawal","Xiaofeng Qian","Vinit Sharma","Houlong Zhuang","Sergei V. Kalinin","Bobby G. Sumpter","Ghanshyam Pilania","Pinar Acar","Subhasish Mandal","Kristjan Haule","David Vanderbilt","Karin Rabe","Francesca Tavazza"],"abstract":"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 .","url_abs":"https://arxiv.org/abs/2007.01831v2","url_pdf":"https://arxiv.org/pdf/2007.01831v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"jarvis-an-integrated-infrastructure-for-data","repo_url":"https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"jarvis-an-integrated-infrastructure-for-data","repo_url":"https://github.com/usnistgov/jarvis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"jarvis-dft-formation-energy","name":"JARVIS-DFT","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2007.01831","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}