Skip to content

Commit

Permalink
Update readme.
Browse files Browse the repository at this point in the history
  • Loading branch information
@knc6
knc6 committed Aug 11, 2023
1 parent 4c62b15 commit 17f31e8
Show file tree
Hide file tree
Showing 2 changed files with 24 additions and 389 deletions.
368 changes: 2 additions & 366 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -16,375 +16,11 @@
<!-- [![image](https://img.shields.io/travis/usnistgov/jarvis/master.svg?label=Travis%20CI)](https://travis-ci.org/usnistgov/jarvis) -->
------------------------------------------------------------------------

# Table of Contents
* [Introduction](#intro)
* [Documentation](#doc)
* [Capabilities](#cap)
* [Installation](#install)
* [Example function](#example)
* [Citation](#cite)
* [References](#refs)
* [How to contribute](#contrib)
* [Correspondence](#corres)
* [Funding support](#fund)
* [Code of conduct](#conduct)
* [Module structure](#module)

<a name="intro"></a>
# JARVIS-Tools (Introduction)
# 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](https://www.nature.com/articles/s41524-020-00440-1)
and [YouTube
videos](https://www.youtube.com/watch?v=P0ZcHXOC6W0&feature=emb_title&ab_channel=JARVIS-repository)

[![image](https://www.ctcms.nist.gov/~knc6/images/logo/jarvis-mission.png)](https://jarvis.nist.gov/)

<a name="doc"></a>
## Documentation

> <https://pages.nist.gov/jarvis>
<a name="cap"></a>
## 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](https://github.com/usnistgov/jarvis/blob/master/DatasetSummary.rst)
.
<a name="install"></a>
## 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

<a name="example"></a>
## 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

> 1. <https://pages.nist.gov/jarvis/tutorials/>
> 2. <https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks>
> 3. <https://github.com/usnistgov/jarvis/tree/master/jarvis/tests/testfiles>
<a name="cite"></a>
## 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).
<a name="refs"></a>
## References

Please see [Publications related to
JARVIS-Tools](https://pages.nist.gov/jarvis/publications/)

<a name="contrib"></a>
## How to contribute

[![image](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)

For detailed instructions, please see [Contribution
instructions](https://github.com/usnistgov/jarvis/blob/master/Contribution.rst)

<a name="corres"></a>
## Correspondence

Please report bugs as Github issues
(<https://github.com/usnistgov/jarvis/issues>) or email to
<[email protected]>.

<a name="fund"></a>
## Funding support

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

<a name="conduct"></a>
## Code of conduct

Please see [Code of
conduct](https://github.com/usnistgov/jarvis/blob/master/CODE_OF_CONDUCT.md)

<a name="module"></a>
## 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
Detailed documentation available at: https://pages.nist.gov/jarvis/
Loading

0 comments on commit 17f31e8

Please sign in to comment.