Metadata-Version: 2.1
Name: jarvis-tools
Version: 2020.9.22
Summary: jarvis-tools: an open-source software package for data-driven atomistic materials design. https://jarvis.nist.gov/
Home-page: https://github.com/usnistgov/jarvis
Author: Kamal Choudhary
Author-email: kamal.choudhary@nist.gov
License: NIST
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        ========================================================================================
        
        JARVIS-Tools: an open-source software package for data-driven atomistic materials design
        =========================================================================================
        
        
        NIST-JARVIS (Joint Automated Repository for Various Integrated Simulations) is an integrated framework for computational science using density functional theory,
        classical force-field/molecular dynamics and machine-learning. The jarvis-tools package consists of scripts used in generating and analyzing the dataset. The NIST-JARVIS official website is: https://jarvis.nist.gov . This project is a part of the Materials Genome Initiative (MGI) at NIST (https://mgi.nist.gov/).
        
        For more details, checkout our latest article:  `JARVIS: An Integrated Infrastructure for Data-driven Materials Design <https://arxiv.org/abs/2007.01831>`__
        
        .. image:: https://www.ctcms.nist.gov/~knc6/images/logo/jarvis-mission.png
           :target: https://jarvis.nist.gov/
        
        
        Some important features
        =======================================================================
        
        - **Software workflow tasks**:  VASP, Quantum Espresso, BoltzTrap, Wannier90, LAMMPS, Scikit-learn, TensorFlow, LightGBM.
        
        - **HPC clusters**: PBS and SLURM.
        
        - **Examples**: Notebooks and test scripts to explain the package.
        
        - **Available datasets**: `Summary of several datasets <https://github.com/usnistgov/jarvis/blob/master/DatasetSummary.rst>`__ .
        
        
        Installation
        ---------------
        
        Please see `Installation instructions <https://github.com/usnistgov/jarvis/blob/master/Installation.rst>`__
        
        
        Example Jupyter notebooks
        -----------------------------
        
        Please find several `Google Colab Notebooks <https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks>`__
        
        
        Example function
        -----------------
        >>> from jarvis.core.atoms import Atoms
        >>> box = [[2.715, 2.715, 0], [0, 2.715, 2.715], [2.715, 0, 2.715]]
        >>> coords = [[0, 0, 0], [0.25, 0.25, 0.25]]
        >>> elements = ["Si", "Si"]
        >>> Si = Atoms(lattice_mat=box, coords=coords, elements=elements)
        >>> density = round(Si.density,2)
        >>> print (density)
        2.33
        >>>
        >>> from jarvis.db.figshare import data
        >>> dft_3d = data(dataset='dft_3d')
        >>> print (len(dft_3d))
        36099
        
        
        References
        -----------------
        
        Please see `Publications related to JARVIS-Tools <https://github.com/usnistgov/jarvis/blob/master/Publications.rst>`__
        
        External links
        -----------------------------------------
        
              https://figshare.com/authors/Kamal_Choudhary/4445539
        
              https://jarvis-materials-design.github.io/dbdocs/
                 
              https://pypi.org/project/jarvis-tools
              
              https://jarvis-tools.readthedocs.io/en/latest/
              
              https://www.slideshare.net/KAMALCHOUDHARY4
        
           
        
        
        Correspondence
        --------------------
        Please report bugs as Github issues (https://github.com/usnistgov/jarvis/issues) or email to kamal.choudhary@nist.gov.
        
        Funding support
        --------------------
        
        NIST-MGI (https://www.nist.gov/mgi).
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Provides-Extra: ai
Provides-Extra: babel
Provides-Extra: doc
