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. Access to the database and web-apps requires a user-credential. User-registration is free, click on the Login/Sign Up button above. Find more details about JARVIS in the article: https://www.nature.com/articles/s41524-020-00440-1

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 Link
Tools pypi Pypi repository of tools Link
Workshops JARVIS-related workshops Link
SlideShare Collection of presentation slides Link

Statistics

JARVIS-FF materials (2538)

JARVIS-DFT materials (55286)

JARVIS-ML (1429605)

OptB88vdW bandgaps and formation energies (55286)

TBmBJ bandgaps (17094)

Elastic Tensors (17402)

Topological SOC spillage (11383)

Infrared intensities (4801)

Dielectric function (15860)

2D exfoliation energy (812)

Carrier effective mass (17642)

Piezoelectric tensors (4801)

Seebeck coeff. (23210)

Electric field gradient (11865)

Solar-SLME (8614)

JARVIS-WannierTB (1771)

JARVIS-STM (1432)

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.