Jarvis-Tools

Official Resources

• Homepage: https://jarvis-tools.readthedocs.io/
• Documentation: https://jarvis-tools.readthedocs.io/
• Source Repository: https://github.com/usnistgov/jarvis
• License: NIST License

Overview

jarvis-tools is the open-source Python software package that powers the JARVIS database. It provides a suite of tools for designing, managing, and analyzing atomistic simulations (DFT, MD) and applying machine learning to materials data. It supports VASP, Quantum ESPRESSO, LAMMPS, and other codes.

Scientific domain: Materials informatics, automation, machine learning
Target user community: Users of JARVIS database, high-throughput researchers

Capabilities (CRITICAL)

• Automation: Workflows for VASP (TB-mBJ, SOC), QE, and LAMMPS.
• Analysis: Band structure, DOS, topological invariants, elastic tensors, STM images.
• Machine Learning: Graph Convolutional Networks (GCN), descriptors, and pre-trained models.
• Database: Tools to interact with and download JARVIS datasets.
• Wannier: Tight-binding hamiltonian generation via Wannier90.

Sources: jarvis-tools documentation

Inputs & Outputs

• Input formats: Atomic structures (POSCAR, CIF), calculation inputs
• Output data types: JSON, XML, Plots

Interfaces & Ecosystem

• Codes: VASP, QE, LAMMPS, Wannier90, Boltztrap.
• ML: DGL, PyTorch, Scikit-learn.
• JARVIS: Official API.

Workflow and Usage

1. Install: pip install jarvis-tools
2. Download data:

   from jarvis.db.figshare import data
   d = data('dft_3d')
   

3. Run ML model: AL = GraphConv(config).train(...)

Performance Characteristics

• Comprehensive suite for 2D and 3D materials
• Strong focus on post-processing and ML

Application Areas

• High-throughput screening
• Machine learning model development
• Electronic structure analysis

Community and Support

• Developed by NIST
• Active development

Verification & Sources

Primary sources:

1. GitHub: https://github.com/usnistgov/jarvis

Confidence: VERIFIED

Verification status: ✅ VERIFIED

• Website: ACTIVE
• Documentation: COMPREHENSIVE
• Source: OPEN (GitHub)
• Development: ACTIVE
• Applications: JARVIS ecosystem, ML, DFT automation