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 and YouTube videos

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 .

Installation

>>> pip install -U jarvis-tools

>>> conda install -c conda-forge jarvis-tools

For detailed instructions, please see Installation instructions

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

https://jarvis-materials-design.github.io/dbdocs/tutorials

https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks

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

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

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

RiverTate/jarvis