Metadata-Version: 2.1
Name: jarvis-tools
Version: 2021.7.4
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
        =========================================================================================
        
        The JARVIS-Tools is an open-access software package for atomistic data-driven materials desgin. JARVIS-Tools can be used for a) setting up calculations, b) analysis and informatics, c) plotting, d) database development and e) web-page development.
        
        JARVIS-Tools empowers NIST-JARVIS (Joint Automated Repository for Various Integrated Simulations) repository which is an integrated framework for computational science using density functional theory, classical force-field/molecular dynamics and machine-learning. 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:  `The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design <https://www.nature.com/articles/s41524-020-00440-1>`__ and `YouTube videos <https://www.youtube.com/watch?v=P0ZcHXOC6W0&feature=emb_title&ab_channel=JARVIS-repository>`__ 
        
        .. image:: https://www.ctcms.nist.gov/~knc6/images/logo/jarvis-mission.png
           :target: https://jarvis.nist.gov/
        
        
        Documentation
        -----------------------------------------
        
              https://jarvis-tools.readthedocs.io
        
              https://jarvis-materials-design.github.io/dbdocs/
        
        
        Capabilities
        -----------------------------------------
        
        - **Software workflow tasks for preprcessing, executing and post-processing**:  VASP, Quantum Espresso, Wien2k BoltzTrap, Wannier90, LAMMPS, Scikit-learn, TensorFlow, LightGBM, Qiskit, Tequila, Pennylane, DGL, PyTorch.
        
        - **Several examples**: Notebooks and test scripts to explain the package.
        
        - **Several analysis tools**: Atomic structure, Electronic structure, Spacegroup, Diffraction, 2D materials and other vdW bonded systems, Mechanical, Optoelectronic, Topological, Solar-cell, Thermoelectric, Piezoelectric, Dielectric, STM, Phonon, Dark matter, Wannier tight binding models, Point defects, Heterostructures, Magnetic ordering, Images, Spectrum etc.
        
        - **Database upload and download**: Download JARVIS databases such as JARVIS-DFT, FF, ML, WannierTB, Solar, STM and also external databases such as Materials project, OQMD, AFLOW etc.
        
        - **Access raw input/output files**: Download input/ouput files for JARVIS-databases to enhance reproducibility.
        
        - **Train machine learning models**: Use different descriptors, graphs and datasets for training machine learning models.
        
        - **HPC clusters**: Torque/PBS and SLURM.
        
        - **Available datasets**: `Summary of several datasets <https://github.com/usnistgov/jarvis/blob/master/DatasetSummary.rst>`__ .
        
        
        Installation
        ---------------
        
        >>> pip install -U jarvis-tools
        
        or
        
        >>> conda install -c conda-forge jarvis-tools
        
        For detailed instructions, please see `Installation instructions <https://github.com/usnistgov/jarvis/blob/master/Installation.rst>`__
        
        
        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))
        48527
        >>> from jarvis.io.vasp.inputs import Poscar
        >>> for i in dft_3d:
        ...     atoms = Atoms.from_dict(i['atoms'])
        ...     poscar = Poscar(atoms)
        ...     jid = i['jid']
        ...     filename = 'POSCAR-'+jid+'.vasp'
        ...     poscar.write_file(filename)
        >>> dft_2d = data(dataset='dft_2d')
        >>> print (len(dft_2d))
        1070
        >>> for i in dft_2d:
        ...     atoms = Atoms.from_dict(i['atoms'])
        ...     poscar = Poscar(atoms)
        ...     jid = i['jid']
        ...     filename = 'POSCAR-'+jid+'.vasp'
        ...     poscar.write_file(filename)
        >>> # Example to parse DOS data from JARVIS-DFT webpages
        >>> from jarvis.db.webpages import Webpage
        >>> from jarvis.core.spectrum import Spectrum
        >>> import numpy as np
        >>> new_dist=np.arange(-5, 10, 0.05)
        >>> all_atoms = []
        >>> all_dos_up = []
        >>> all_jids = []
        >>> for ii,i in enumerate(dft_3d):
              all_jids.append(i['jid'])
        ...   try:
        ...     w = Webpage(jid=i['jid'])
        ...     edos_data = w.get_dft_electron_dos()
        ...     ens = np.array(edos_data['edos_energies'].strip("'").split(','),dtype='float')
        ...     tot_dos_up = np.array(edos_data['total_edos_up'].strip("'").split(','),dtype='float')
        ...     s = Spectrum(x=ens,y=tot_dos_up)
        ...     interp = s.get_interpolated_values(new_dist=new_dist)
        ...     atoms=Atoms.from_dict(i['atoms'])
        ...     ase_atoms=atoms.ase_converter()
        ...     all_dos_up.append(interp)
        ...     all_atoms.append(atoms)
        ...     all_jids.append(i['jid'])
        ...     filename=i['jid']+'.cif'
        ...     atoms.write_cif(filename)
        ...     break
        ...   except Exception as exp :
        ...     print (exp,i['jid'])
        ...     pass
        
        
        
        Find more examples at
        
              1) https://jarvis-materials-design.github.io/dbdocs/tutorials
              
              2) https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks
              
              3) https://github.com/usnistgov/jarvis/tree/master/jarvis/tests/testfiles
              
        
        Citing
        --------------------
              
        Please cite the following if you happen to use JARVIS-Tools for a publication.
        
        https://www.nature.com/articles/s41524-020-00440-1
        
            Choudhary, K. et al. The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design. npj Computational Materials, 6(1), 1-13 (2020).
        
        
        References
        -----------------
        
        Please see `Publications related to JARVIS-Tools <https://jarvis-materials-design.github.io/dbdocs/publications/>`__
        
        
        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).
        
        Code of conduct
        --------------------
        
        Please see `Code of conduct <https://github.com/usnistgov/jarvis/blob/master/CODE_OF_CONDUCT.md>`__
        
        Module structure
        --------------------
        ::
        
            jarvis/
            ├── ai
            │   ├── descriptors
            │   │   ├── cfid.py
            │   │   ├── coulomb.py
            │   ├── gcn
            │   ├── pkgs
            │   │   ├── lgbm
            │   │   │   ├── classification.py
            │   │   │   └── regression.py
            │   │   ├── sklearn
            │   │   │   ├── classification.py
            │   │   │   ├── hyper_params.py
            │   │   │   └── regression.py
            │   │   └── utils.py
            │   ├── uncertainty
            │   │   └── lgbm_quantile_uncertainty.py
            ├── analysis
            │   ├── darkmatter
            │   │   └── metrics.py
            │   ├── defects
            │   │   ├── surface.py
            │   │   └── vacancy.py
            │   ├── diffraction
            │   │   └── xrd.py
            │   ├── elastic
            │   │   └── tensor.py
            │   ├── interface
            │   │   └── zur.py
            │   ├── magnetism
            │   │   └── magmom_setup.py
            │   ├── periodic
            │   │   └── ptable.py
            │   ├── phonon
            │   │   ├── force_constants.py
            │   │   └── ir.py
            │   ├── solarefficiency
            │   │   └── solar.py
            │   ├── stm
            │   │   └── tersoff_hamann.py
            │   ├── structure
            │   │   ├── neighbors.py
            │   │   ├── spacegroup.py
            │   ├── thermodynamics
            │   │   ├── energetics.py
            │   ├── topological
            │   │   └── spillage.py
            ├── core
            │   ├── atoms.py
            │   ├── composition.py
            │   ├── graphs.py
            │   ├── image.py
            │   ├── kpoints.py
            │   ├── lattice.py
            │   ├── pdb_atoms.py
            │   ├── specie.py
            │   ├── spectrum.py
            │   └── utils.py
            ├── db
            │   ├── figshare.py
            │   ├── jsonutils.py
            │   ├── lammps_to_xml.py
            │   ├── restapi.py
            │   ├── vasp_to_xml.py
            │   └── webpages.py
            ├── examples
            │   ├── lammps
            │   │   ├── jff_test.py
            │   │   ├── Al03.eam.alloy_nist.tgz
            │   ├── vasp
            │   │   ├── dft_test.py
            │   │   ├── SiOptb88.tgz
            ├── io
            │   ├── boltztrap
            │   │   ├── inputs.py
            │   │   └── outputs.py
            │   ├── calphad
            │   │   └── write_decorated_poscar.py
            │   ├── lammps
            │   │   ├── inputs.py
            │   │   └── outputs.py
            │   ├── pennylane
            │   │   ├── inputs.py
            │   ├── phonopy
            │   │   ├── fcmat2hr.py
            │   │   ├── inputs.py
            │   │   └── outputs.py
            │   ├── qe
            │   │   ├── inputs.py
            │   │   └── outputs.py
            │   ├── qiskit
            │   │   ├── inputs.py
            │   ├── tequile
            │   │   ├── inputs.py
            │   ├── vasp
            │   │   ├── inputs.py
            │   │   └── outputs.py
            │   ├── wannier
            │   │   ├── inputs.py
            │   │   └── outputs.py
            │   ├── wanniertools
            │   │   ├── inputs.py
            │   │   └── outputs.py
            │   ├── wien2k
            │   │   ├── inputs.py
            │   │   ├── outputs.py
            ├── tasks
            │   ├── boltztrap
            │   │   └── run.py
            │   ├── lammps
            │   │   ├── templates
            │   │   └── lammps.py
            │   ├── phonopy
            │   │   └── run.py
            │   ├── vasp
            │   │   └── vasp.py
            │   ├── queue_jobs.py
            ├── tests
            │   ├── testfiles
            │   │   ├── ai
            │   │   ├── analysis
            │   │   │   ├── darkmatter
            │   │   │   ├── defects
            │   │   │   ├── elastic
            │   │   │   ├── interface
            │   │   │   ├── magnetism
            │   │   │   ├── periodic
            │   │   │   ├── phonon
            │   │   │   ├── solar
            │   │   │   ├── stm
            │   │   │   ├── structure
            │   │   │   ├── thermodynamics
            │   │   │   ├── topological
            │   │   ├── core
            │   │   ├── db
            │   │   ├── io
            │   │   │   ├── boltztrap
            │   │   │   ├── calphad
            │   │   │   ├── lammps
            │   │   │   ├── pennylane
            │   │   │   ├── phonopy
            │   │   │   ├── qiskit
            │   │   │   ├── qe
            │   │   │   ├── tequila
            │   │   │   ├── vasp
            │   │   │   ├── wannier
            │   │   │   ├── wanniertools
            │   │   │   ├── wien2k
            │   │   ├── tasks
            │   │   │   ├── test_lammps.py
            │   │   │   └── test_vasp.py
            └── README.rst
            
        
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
