JARVIS-Tools

About

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, e) machine-learning, and f) 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

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.

Installation

Using pip

>>> pip install -U jarvis-tools

or

Using conda

First create a conda environment: Install miniconda environment from https://conda.io/miniconda.html Based on your system requirements, you’ll get a file something like ‘Miniconda3-latest-XYZ’.

>>> bash Miniconda3-latest-Linux-x86_64.sh (for linux)
>>> bash Miniconda3-latest-MacOSX-x86_64.sh (for Mac)

Now let’s create a conda environment and install jarvis-tools from conda-forge:

>>> conda create --name my_jarvis python=3.8
>>> source activate my_jarvis
>>> conda install -c conda-forge jarvis-tools

Please make sure to use python>3.7.

Example function

Create Atoms object:

>>> 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

Obtain JARVIS-DFT 3D dataset with various materials and their properties

>>> from jarvis.db.figshare import data
>>> dft_3d = data(dataset='dft_3d')
>>> print (len(dft_3d))
48527

Write to POSCAR files to visualize/analyze in VESTA or other packages

>>> 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)

Get JARVIS-DFT 2D dataset

>>> 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 XML 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/

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

@article{choudhary2020joint,

title={The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design}, author={Choudhary, Kamal and Garrity, Kevin F and Reid, Andrew CE and DeCost, Brian and Biacchi, Adam J and Walker, Angela R Hight and Trautt, Zachary and Hattrick-Simpers, Jason and Kusne, A Gilad and Centrone, Andrea and others}, journal={npj Computational Materials}, volume={6}, number={1}, pages={1–13}, year={2020}, publisher={Nature Publishing Group}

}

Module details

• Module Index

• Index

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