Metadata-Version: 2.1
Name: jarvis-tools
Version: 2019.7.31
Summary: High throughput computation with density functional theory, molecular dynamics and machine learning. https://jarvis.nist.gov/
Home-page: https://github.com/usnistgov/jarvis
Author: Kamal Choudhary
Author-email: kamal.choudhary@nist.gov
License: MIT
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        JARVIS
        ==========
        
        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: https://jarvis.nist.gov
        
        .. image:: https://github.com/knc6/jarvis/blob/master/jarvis/tools/jarvis-git.JPG
                :target: https://jarvis.nist.gov/
        .. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/statistics.JPG
                :target: https://jarvis.nist.gov/
        
        Installing JARVIS
        --------------------
        
        - We recommend installing miniconda environment from https://conda.io/miniconda.html ::
        
              bash Miniconda3-latest-Linux-x86_64.sh (for linux)
              bash Miniconda3-latest-MacOSX-x86_64.sh (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 https://github.com/usnistgov/jarvis.git
              cd jarvis
              python setup.py 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`_.
        
        .. _`Nix instructions`: ./nix/README.md
        
        Jupyter notebooks
        -----------------
        
        - Python for beginners:
        
        .. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/novice.JPG
                :target: https://colab.research.google.com/github/knc6/jarvis/blob/master/jarvis/colab/python_novice_notebook.ipynb
        
        - JARVIS-DFT data analysis:
        
        .. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/jdft.JPG
                :target: https://colab.research.google.com/github/knc6/jarvis/blob/master/jarvis/colab/jarvis_dft_explore_notebook.ipynb
        
        - JARVIS-ML training:
        
        .. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/jml_train.JPG
                :target: https://colab.research.google.com/github/knc6/jarvis/blob/master/jarvis/colab/jarvis_ml_quick_train_notebook.ipynb
        
        - Comparing ML algorithms:
        
        .. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/compareml.JPG
                :target: https://colab.research.google.com/github/knc6/jarvis/blob/master/jarvis/colab/compare_ml_algorithms_notebook.ipynb
        
        - JARVIS-FF data-analysis:
        
        .. image:: https://github.com/knc6/jarvis/blob/master/jarvis/colab/colab_figures/jff.JPG
                :target: https://colab.research.google.com/github/knc6/jarvis/blob/master/jarvis/colab/jarvis_ff_explore_notebook.ipynb
        
        - See more in the plot-gallery below
        
        
        References
        -----------------
        
        - JARVIS-FF:
              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
              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
        - JARVIS-DFT:
              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
              4) Computational Screening of High-performance Optoelectronic Materials using OptB88vdW and TBmBJ Formalisms, Scientific Data 5, 180082 (2018).https://www.nature.com/articles/sdata201882
              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
              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
              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
              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
              9) Data-driven Discovery of 3D and 2D Thermoelectric Materials , https://arxiv.org/abs/1903.06651.
        - JARVIS-ML:
              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
              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
        
        
        
        Pypi, Readthedocs and Slideshare links
        -----------------------------------------
              https://pypi.org/project/jarvis-tools
              
              https://jarvis-tools.readthedocs.io/en/latest/
              
              https://www.slideshare.net/KAMALCHOUDHARY4
        
        Running the examples
        --------------------------------
        - For running high-throughput calculations, set HPC/system related information in env_variables
        - Run py.test in tests folder to ensure basic setup
        - LAMMPS example:
              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.
        - VASP example:
              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. 
        - ML example:
              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
        - Access to JARVIS database:
               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. 
               We can also load the dataset using python scripts similar to https://github.com/knc6/jarvis/blob/master/jarvis/db/static/explore_db.py .
        - Uploading your data using JARVIS-API:
               In addition to downloading/browsing through the JARVIS-database, one can also upload their data and query using JARVIS-API. Follow the instructions in https://github.com/usnistgov/jarvis/blob/master/jarvis/db/mdcs/mdcs_api.py
        
        Founders
        --------------------------------
        Kamal Choudhary, Francesca Tavazza (NIST)
        
        Contributors
        -----------------------------------
        Daniel Wheeler, Faical Yannick Congo, Kevin Garrity, Brian DeCost, Adam Biacchi,
        Lucas Hale, Andrew Reid, Marcus Newrock (NIST)
        
        
        Plot-gallery with additional jupyter notebooks
        -----------------------------------------------------
        .. class:: center
        .. image:: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/RDF.png
        
        :Notebook: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/RDF%2CPRDF%2CADF%2CDDF.ipynb
        
        .. image:: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/ADF-a.png
        
        :Notebook: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/RDF%2CPRDF%2CADF%2CDDF.ipynb
        
        .. image:: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/ADF-b.png
        
        :Notebook: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/RDF%2CPRDF%2CADF%2CDDF.ipynb
        
        .. image:: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/DDF.png
        
        :Notebook: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/RDF%2CPRDF%2CADF%2CDDF.ipynb
        
        .. image:: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/bandstr.jpg
        
        :Notebook: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/band_structure.ipynb
        
        .. image:: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/Dos.png
        
        :Notebook: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/band_structure.ipynb
        
            
        .. image:: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/Wulff.png
        
        :Notebook: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/Wulff.ipynb
        
        .. image:: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/BoltzTrap.png
        
        :Notebook: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/Boltztrap.ipynb
        
        .. image:: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/kp_converg.png
        
        :Notebook: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/Convergence.ipynb
        
        .. image:: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/en_converg.png
        
        :Notebook: https://github.com/usnistgov/jarvis/blob/master/jarvis/db/static/Convergence.ipynb
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
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: doc
Provides-Extra: babel
