NIST-JARVIS The JARVIS (Joint Automated Repository for Various Integrated Simulations) is an infrastructure designed to automate materials discovery and optimization using classical force-field, density functional theory, machine learning, quantum computation calculations and experiments.

User-registration is free, click on the Login/Sign up button. Access to the database and web apps requires user credentials. Find more details about JARVIS in: Nature Portfolio, AIP and other publications. For upcoming events, checkout the JARVIS-Events page.

Overview

Established: January, 2017
Funding: MGI, CHIPS
Published articles: 50+
Citations: 4500+
Users: 150000+
Data downloads: 1.5 million+
Materials: 80000+
Properties: 1 million+
Several databases, webapps, tutorials and tools.

Integrated tools

JARVIS Mission Logo

For a given materials performance metric, several JARVIS components can work together to design optimized or completely new materials.

Introduction video

This video presents a short ~1 minute introduction to JARVIS.

Databases

Name URLs Description
1. JARVIS-DFT https://jarvis.nist.gov/jarvisdft Density Functional Theory (DFT) calculation database for 80000+ materials, millions of properties using OptB88vdW and TBmBJ methods, with/without spin-orbit coupling.
2. JARVIS-FF https://jarvis.nist.gov/jarvisff Classical force-field (FF) calculation database for ~2000 materials, >100 Classical FFs
3. JARVIS-QETB https://jarvis.nist.gov/jarvisqetb Density functional theory database for 800000+ materials using PBEsol method.
4. JARVIS-ChemNLP https://jarvis.nist.gov/jarvischemnlp Database of open access papers based on chemical formula filters.
5. JARVIS-Leaderboard https://pages.nist.gov/jarvis_leaderboard/ A large collection of benchmark methods for materials design methods including theory and experiments, to enhance transparency and reproducibility, with >300 benchmarks, 9 million data points
6. JARVIS-Tools-Notebooks https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks A Jupyter/Google-Colab Notebook Collection for Materials Design.
7. JARVIS-OPTIMADE https://jarvis.nist.gov/optimade/jarvisdft/ Open Databases Integration for Materials Design (OPTIMADE) API for JARVIS-DFT data.
9. JARVIS-SuperConDB https://jarvis.nist.gov/supercon Superconductor database for 3D and 2D materials with DFT.
10. JARVIS-InterfacDB https://jarvis.nist.gov/interfacedb Interface database.
11. JARVIS-Universal Search https://jarvis.nist.gov/jarvisus Integrated search for multiple databases.
12. JARVIS-DMFT https://jarvis.nist.gov/jarvisbdft Dynamical Mean Field Theory Database.
13. FigShare curated datasets https://pages.nist.gov/jarvis/databases/ FigShare based databases.
14. JARVIS-DFT2 https://jarvis.nist.gov/jarvisdft2 Density Functional Theory (DFT) calculation database for 80000+ materials, millions of properties using OptB88vdW and TBmBJ methods, with/without spin-orbit coupling.

WebApps

Name URLs Description
1. ALIGNN property predictor https://jarvis.nist.gov/jalignn/ Atomistic Line Graph Neural Network for fast property prediction.
2. ALIGNN Force-field https://jarvis.nist.gov/jalignnff/ Atomistic Line Graph Neural Network Force-field for fast structure optimization.
3. Solar cells https://jarvis.nist.gov/jarvissolar Predict theoretical solar cell performance of a material.
4. Direct Air Capture https://jarvis.nist.gov/jdac Predicts CO2 adsorption isotherm of an input metal organic framework.
5. Scanning Tunneling Microscopy https://jarvis.nist.gov/jarvisstm Tersoff-Hamann approach based scanning tunneling microscopy images.
6. Scanning Transmission Electron Microscopy https://jarvis.nist.gov/jstem Scanning transmission electron microscopy (STEM) using convolution approximation.
7. Heterostructure https://jarvis.nist.gov/jarvish Zur algorithm based designing of material interfaces/heterostructures.
8. Catalysis https://jarvis.nist.gov/jcatalysis Predicts adsorption energy of a molecule on a substrate.
9. JARVIS-Visualization https://jarvis.nist.gov/jarvisviz Quick atomic structure visualizer.
10. JARVIS-XRD https://jarvis.nist.gov/jxrd Predicts theoretical X-ray diffraction patterns.
11. JARVIS-WTBH https://jarvis.nist.gov/jarviswtb Predicts the Wannier Tight-binding Hamiltonian derived properties.
13. JARVIS-Battery https://jarvis.nist.gov/jbattery Predict volumetric/gravimetric capacities, voltage profiles.
14. JARVIS-ML/CFID https://jarvis.nist.gov/jarvisml Classical Force-Field Inspired Descriptors based predicted properties.

Tools

Name URLs Description
1. JARVIS-Tools https://github.com/usnistgov/jarvis JARVIS-Tools: An open-source software package for data-driven atomistic materials design.
2. ALIGNN https://github.com/usnistgov/alignn ALIGNN: Atomistic Line Graph Neural Network and force-field.
3. JARVIS-Leaderboard https://github.com/usnistgov/jarvis_leaderboard JARVIS-Leaderboard: Explore State-of-the-Art Materials Design Methods and Reproducible Benchmarks.
4. AtomGPT https://github.com/usnistgov/atomgpt AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design.
5. ChemNLP https://github.com/usnistgov/chemnlp ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data.
6. AtomVision https://github.com/usnistgov/atomvision AtomVision: Deep learning framework for atomistic image data.
7. AtomQC https://github.com/usnistgov/atomqc AtomQC: Atomistic Calculations on Quantum Computers.
8. InterMat https://github.com/usnistgov/intermat InterMat: Interface materials design toolkit.
9. DefectMat https://github.com/usnistgov/defectmat DefectMat: Defect materials design toolkit.
10. TB3Py https://github.com/usnistgov/tb3py TB3Py: Two- and three-body tight-binding calculations for materials.

Tutorials

Name URLs Description
1. JARVIS-Tools-Notebooks https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks A Jupyter/Google-Colab Notebook Collection for Materials Design.
2. JARVIS-Tools-Tutorials https://pages.nist.gov/jarvis/tutorials/ Workflow setup and analysis documentation.
3. JARVIS-Leaderboard-Notebooks https://pages.nist.gov/jarvis_leaderboard/notebooks/ Notebooks for contributing to leaderboard.

Outreach

Name URLs Description
1. JARVIS-Publications https://pages.nist.gov/jarvis/publications/ Peer-reviewed publications from the JARVIS team.
2. JARVIS-School https://jarvis.nist.gov/events/school A tutorial/hands-on session to introduce open-access databases and tools for materials-design.
3. AIMS workshop https://jarvis.nist.gov/events/aims Artificial Intelligence for Materials Science workshop.
4. QMMS workshop https://jarvis.nist.gov/events/qmms Quantum Matters in Materials Science workshop.
5. ReproHack https://forms.gle/MfZsW7H7PpLMf8Yb9 Reproducibility Hackathon.
6. SlideShare https://www.slideshare.net/KAMALCHOUDHARY4 Collection of presentation slides.
7. Google group https://groups.google.com/a/list.nist.gov/g/jarvis Google group JARVIS related announcements.
8. Guidance/Help/Issues https://github.com/usnistgov/jarvis/issues Comment/questions/concerns to share.
9. MATSCI Forum https://matsci.org/c/jarvis/48/ Community/discourse on MATSCI.

Statistics

JARVIS-DFT materials (77096)

JARVIS-FF materials (2538)

JARVIS-ML (1429605)

OptB88vdW electronic bandgaps and formation energies (77096)

TBmBJ bandgaps (18293)

Elastic Tensors (25513)

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 (226778)




Founder and developer: Dr. Kamal Choudhary

Contributors: (Drs.) Francesca Tavazza, Kevin F. Garrity, Daniel Wines, Andrew C. E. Reid, Brian DeCost, Adam J. Biacchi, Ramya Gurunathan, Taner Yildirim, 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 Agarwal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei Kalinin, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, Kristjan Haule, David Vanderbilt, Igor Mazin, Karin Rabe, Mark Kasule.