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High throughput computation with density functional theory, molecular dynamics and machine learning.

Project description


Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated framework for computational science using density functional theory, classical force-field/molecular dynamics and machine-learning. The jarvis-tools package can be used for high-throughput computation, data-analysis, and training machine-learning models. Some of the packages used in the jarvis-tools package are shown below. JARVIS-official website:

Installing JARVIS

  • We recommend installing miniconda environment from

    bash (for linux)
    bash (for Mac)
    Download 32/64 bit python 3.6 miniconda exe and install (for windows)
    Now, let's make a conda environment just for JARVIS::
    conda create --name my_jarvis python=3.6
    source activate my_jarvis
  • Git clone install (Recommended):

    pip install numpy scipy matplotlib
    git clone
    cd jarvis
    python install
  • Alternative pip install:

    pip install numpy scipy matplotlib
    pip install jarvis-tools
  • Alternative nix install:: Nix allows a robust and reproducible package for Linux. To generate a Nix environment for using JARVIS, follow the Nix instructions.

Jupyter notebooks

  • Python for beginners:
  • JARVIS-DFT data analysis:
  • JARVIS-ML training:
  • Comparing ML algorithms:
  • JARVIS-FF data-analysis:
  • See more in the plot-gallery below


    1. Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface, Nature: Sci Data. 4, 160125 (2017).
    2. High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields, J. Phys. Cond. Matt. 30, 395901(2018).
    1. High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory, Scientific Reports 7, 5179 (2017).
    2. Computational Screening of High-performance Optoelectronic Materials using OptB88vdW and TBmBJ Formalisms, Scientific Data 5, 180082 (2018).
    3. Elastic properties of bulk and low-dimensional materials using van der Waals density functional, Phys. Rev. B, 98, 014107 (2018).
    4. Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations, Comp. Mat. Sci. 161, 300 (2019).
    5. High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage, Nature: Sci. Rep. 9, 8534,(2019),
    6. Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods, Chem. Mater.,
    7. Data-driven Discovery of 3D and 2D Thermoelectric Materials ,
    1. Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape, Phys. Rev. Mat., 2, 083801 (2018).,
    2. Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics, MRS Comm., 1-18

Running the examples


Kamal Choudhary, Francesca Tavazza (NIST)


Daniel Wheeler, Faical Yannick Congo, Kevin Garrity, Brian DeCost, Adam Biacchi, Lucas Hale, Andrew Reid, Marcus Newrock (NIST)

Project details

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