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        "author": "Kamal Choudhary",
        "author_email": "kamal.choudhary@nist.gov",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering"
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        "description": ".. class:: center\n.. image:: https://circleci.com/gh/usnistgov/jarvis.svg?style=shield\n          :target: https://circleci.com/gh/usnistgov/jarvis\n.. image:: https://travis-ci.org/usnistgov/jarvis.svg?branch=master\n       :target: https://travis-ci.org/usnistgov/jarvis\n.. image:: https://ci.appveyor.com/api/projects/status/d8na8vyfm7ulya9p/branch/master?svg=true\n       :target: https://ci.appveyor.com/project/knc6/jarvis-63tl9\n.. image:: https://api.codacy.com/project/badge/Grade/be8fa78b1c0a49c280415ce061163e77\n       :target: https://www.codacy.com/app/knc6/jarvisutm_source=github.com&utm_medium=referral&utm_content=usnistgov/jarvis&utm_campaign=Badge_Grade\n.. image::  https://img.shields.io/pypi/dm/jarvis-tools.svg      \n        :target: https://img.shields.io/pypi/dm/jarvis-tools.svg\n.. image:: https://pepy.tech/badge/jarvis-tools\n        :target: https://pepy.tech/badge/jarvis-tools\n.. image:: https://codecov.io/gh/knc6/jarvis/branch/master/graph/badge.svg\n        :target: https://codecov.io/gh/knc6/jarvis\n\nJARVIS\n==========\n\nJoint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated framework for computational science using density functional theory,\nclassical 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: https://jarvis.nist.gov\n\n.. image:: https://github.com/knc6/jarvis/blob/master/jarvis/tools/jarvis-git.JPG\n        :target: https://jarvis.nist.gov/\n.. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/statistics.JPG\n        :target: https://jarvis.nist.gov/\n\nInstalling JARVIS\n--------------------\n\n- We recommend installing miniconda environment from https://conda.io/miniconda.html ::\n\n      bash Miniconda3-latest-Linux-x86_64.sh (for linux)\n      bash Miniconda3-latest-MacOSX-x86_64.sh (for Mac)\n      Download 32/64 bit python 3.6 miniconda exe and install (for windows)\n      Now, let's make a conda environment just for JARVIS::\n      conda create --name my_jarvis python=3.6\n      source activate my_jarvis\n\n- Git clone install (Recommended)::\n\n      pip install numpy scipy matplotlib\n      git clone https://github.com/usnistgov/jarvis.git\n      cd jarvis\n      python setup.py install\n\n\n- Alternative pip install::\n\n      pip install numpy scipy matplotlib\n      pip install jarvis-tools\n\n- Alternative nix install::\n  Nix allows a robust and reproducible package for Linux. To generate a Nix environment for using JARVIS, follow the `Nix instructions`_.\n\n.. _`Nix instructions`: ./nix/README.md\n\nJupyter notebooks\n-----------------\n\n- Python for beginners:\n\n.. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/novice.JPG\n        :target: https://colab.research.google.com/github/knc6/jarvis/blob/master/jarvis/colab/python_novice_notebook.ipynb\n\n- JARVIS-DFT data analysis:\n\n.. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/jdft.JPG\n        :target: https://colab.research.google.com/github/knc6/jarvis/blob/master/jarvis/colab/jarvis_dft_explore_notebook.ipynb\n\n- JARVIS-ML training:\n\n.. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/jml_train.JPG\n        :target: https://colab.research.google.com/github/knc6/jarvis/blob/master/jarvis/colab/jarvis_ml_quick_train_notebook.ipynb\n\n- Comparing ML algorithms:\n\n.. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/compareml.JPG\n        :target: https://colab.research.google.com/github/knc6/jarvis/blob/master/jarvis/colab/compare_ml_algorithms_notebook.ipynb\n\n- JARVIS-FF data-analysis:\n\n.. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/jff.JPG\n        :target: https://colab.research.google.com/github/knc6/jarvis/blob/master/jarvis/colab/jarvis_ff_explore_notebook.ipynb\n\n- See more in the plot-gallery below\n\n\nReferences\n-----------------\n\n- JARVIS-FF:\n      1) Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface, Nature: Sci Data. 4, 160125 (2017).https://www.nature.com/articles/sdata2016125\n      2) High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields, J. Phys. Cond. Matt. 30, 395901(2018).http://iopscience.iop.org/article/10.1088/1361-648X/aadaff/meta\n- JARVIS-DFT:\n      3) High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory, Scientific Reports 7, 5179 (2017).https://www.nature.com/articles/s41598-017-05402-0\n      4) Computational Screening of High-performance Optoelectronic Materials using OptB88vdW and TBmBJ Formalisms, Scientific Data 5, 180082 (2018).https://www.nature.com/articles/sdata201882\n      5) Elastic properties of bulk and low-dimensional materials using van der Waals density functional, Phys. Rev. B, 98, 014107 (2018).https://journals.aps.org/prb/abstract/10.1103/PhysRevB.98.014107\n      6) 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).https://www.sciencedirect.com/science/article/pii/S0927025619300813?via%3Dihub\n      7) High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage, Nature: Sci. Rep. 9, 8534,(2019),  https://www.nature.com/articles/s41598-019-45028-y\n      8) Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods, Chem. Mater., https://pubs.acs.org/doi/10.1021/acs.chemmater.9b02166\n      9) Data-driven Discovery of 3D and 2D Thermoelectric Materials , https://arxiv.org/abs/1903.06651.\n- JARVIS-ML:\n      10) Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape, Phys. Rev. Mat., 2, 083801 (2018).,https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.2.083801\n      11) 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 https://doi.org/10.1557/mrc.2019.95\n\n\n\nPypi, Readthedocs and Slideshare links\n-----------------------------------------\n      https://pypi.org/project/jarvis-tools\n\n      https://jarvis-tools.readthedocs.io/en/latest/\n\n      https://www.slideshare.net/KAMALCHOUDHARY4\n\nRunning the examples\n--------------------------------\n- For running high-throughput calculations, set HPC/system related information in env_variables\n- Run py.test in tests folder to ensure basic setup\n- LAMMPS example:\n      An example calculation for Aluminum is given in the lammps folder for running EAM calculation (https://github.com/usnistgov/jarvis/blob/master/jarvis/lammps/examples/basic_input_output.py). Untar the example folder using tar -xvzf Al03.eam.alloy_nist.tgz . Change the 'parameters' variable and run jlammps.py.\n- VASP example:\n      Similarly, an example calculation for Silicon is given in vasp folder (https://github.com/usnistgov/jarvis/blob/master/jarvis/vasp/examples/runstruct_pyvasp.py). The input is a POSCAR file, which is already provided. executable paths, pseudopotential directory path and Special_POTCAR.yaml path needs to be adjusted in joptb88vdw.py top section. The master.py can be submitted to the queuing system with qsub sub.sh. \n- ML example:\n      We trained machine learning models using JARVIS-DFT data on bandgaps, formation energies and elastic modulus and other properties. We used both chemical and structural descriptors during GradientBoostingRegression training. Example of getting 1557 descriptors for a system is given at: https://github.com/usnistgov/jarvis/blob/master/jarvis/sklearn/examples/desc_example.py\n- Access to JARVIS database:\n       Our database is freely available at https://www.ctcms.nist.gov/~knc6/JVASP.html, https://www.ctcms.nist.gov/jarvisml/, https://www.ctcms.nist.gov/~knc6/periodic.html, and https://www.ctcms.nist.gov/~knc6/JLAMMPS.html for JARVIS-DFT, JARVIS-ML and JARVIS-FF. \n       We can also load the dataset using python scripts similar to https://github.com/knc6/jarvis/blob/master/jarvis/db/static/explore_db.py .\n- Uploading your data using JARVIS-API:\n       In addition to downloading/browsing through the JARVIS-database, one can also upload their data and query using JARVIS-API. 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