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
https://jarvis-tools.readthedocs.io
- 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 .
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 3.9 miniconda exe and install (for windows) Now, let's make a conda environment just for JARVIS:: conda create --name my_jarvis python=3.9 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
>>> 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'])
... passFind more examples at
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).
Please see Publications related to JARVIS-Tools
For detailed instructions, please see Contribution instructions
Please report bugs as Github issues (https://github.com/usnistgov/jarvis/issues) or email to [email protected].
NIST-MGI (https://www.nist.gov/mgi).
Please see Code of conduct
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