Table of Contents¶
- Introduction
- Documentation
- Capabilities
- Installation
- Example function
- Citation
- References
- How to contribute
- Correspondence
- Funding support
- Code of conduct
- Module structure
JARVIS-Tools (Introduction)¶
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 articles: The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design, Recent progress in the JARVIS infrastructure for next-generation data-driven materials design, other publications and YouTube videos
Documentation¶
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 3.10 miniconda exe and install (for windows) Now, let's make a conda environment just for JARVIS:: conda create --name my_jarvis python=3.10 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 develop -
Method-4: Note on installing additional dependencies (for developers):
conda env create --name my_jarvis -f environment.yml conda activate my_jarvis conda install pytest coverage codecov git clone https://github.com/usnistgov/jarvis.git cd jarvis git checkout develop python setup.py develop coverage run -m pytest
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))
75993
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))
1109
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
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 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