NIST-JARVIS

JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery and optimization using classical force-field, density functional theory, machine learning calculations and experiments. Find more details in https://arxiv.org/abs/2007.01831

Websites

NIST-JARVIS Resources

Resource Summary Website
Homepage Description and API Link
DFT Density functional theory data Link
FF Evaluation of classical force field Link
ML Machine learning models Link
Tools Scripts for running simulations Link
Downloads Downloadable metadata Link
Notebooks Jupyter/Google-Colab notebooks Link
Heterostructure 2D heterostructure properties Link
STM STM images for 2D materials Link
WannierTB Wannier tight binding models Link
BeyondDFT High-level ab-initio methods Link
Publications JARVIS-related publications Link
Tools docs Documentation (docs) of tools Link
DB docs Documentation on the database Link
Tools pypi Pypi repository of tools Link
Workshops JARVIS-related workshops Link
ResearchG. Social media researchgate page. Link
Twitter Social media twitter page Link
Facebook Social media facebook page Link
Linkedin Social media linkedin page Link
SlideShare Collection of presentation slides Link
YouTube Social media youtube page Link
Google-group Social media google-group Link

Number of materials

JARVIS-FF (1471)

JARVIS-DFT (40345)

JARVIS-ML (1429605)

JARVIS-WTB (1771)

JARVIS-STM (716)

JARVIS-Hetero (226779)




Founder and developer: Dr. Kamal Choudhary

Contributors: (Drs.) Francesca Tavazza, 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 Agarwa, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, Kristjan Haule, David Vanderbilt, Karin Rabe.