jarvis-tools 2023.4.6

jarvis-tools: an open-source software package for data-driven atomistic materials design. https://jarvis.nist.gov/

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JARVIS-Tools

The JARVIS-Tools is an open-access software package for atomistic data-driven materials design. 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 and YouTube videos

Documentation

https://jarvis-tools.readthedocs.io

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 .

Installation

• 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 3.8 miniconda exe and install (for windows)
  Now, let's make a conda environment just for JARVIS::
  conda create --name my_jarvis python=3.8
  source activate my_jarvis

• Method-1: Installation using pip:

  pip install -U jarvis-tools

• Method-2: Installation using conda:

  conda install -c conda-forge jarvis-tools

• Method-3: Installation using setup.py:

  pip install numpy scipy matplotlib
  git clone https://github.com/usnistgov/jarvis.git
  cd jarvis
  python setup.py install

• Note on installing additional dependencies for all modules to function:

  pip install -r dev-requirements.txt

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))
55723
>>> 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))
1079
>>> 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-tools.readthedocs.io/en/master/tutorials.html

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

How to contribute

For detailed instructions, please see Contribution instructions

Correspondence

Please report bugs as Github issues (https://github.com/usnistgov/jarvis/issues) or email to [e-mail].

Funding support

NIST-MGI (https://www.nist.gov/mgi).

Code of conduct

Please see Code of conduct

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