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The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

Published

Author(s)

Kamal Choudhary, Kevin F. Garrity, Andrew C. Reid, Brian DeCost, Adam Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, Aaron Kusne, Andrea Centrone, Albert Davydov, Francesca Tavazza, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, David Vanderbilt, Karin Rabe

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, ≈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.
Citation
npj Computational Materials
Volume
6

Keywords

DFT, FF, ML, AI, JARVIS

Citation

Choudhary, K. , Garrity, K. , Reid, A. , DeCost, B. , Biacchi, A. , , A. , Trautt, Z. , Hattrick-Simpers, J. , Kusne, A. , Centrone, A. , Davydov, A. , Tavazza, F. , Jiang, J. , Pachter, R. , Cheon, G. , Reed, E. , Agrawal, A. , Qian, X. , Sharma, V. , Zhuang, H. , Kalinin, S. , Pilania, G. , Acar, P. , Mandal, S. , Vanderbilt, D. and Rabe, K. (2020), The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design, npj Computational Materials, [online], https://doi.org/10.1038/s41524-020-00440-1, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930588 (Accessed June 17, 2021)
Created November 12, 2020, Updated April 6, 2021