NIST-JARVIS (Joint Automated Repository for Various Integrated Simulations) is an integrated framework for computational science using density functional theory, classical force-field/molecular dynamics and machine-learning. The jarvis-tools package consists of scripts used in generating and analyzing the dataset. 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: JARVIS: An Integrated Infrastructure for Data-driven Materials Design
- Software workflow tasks: VASP, Quantum Espresso, BoltzTrap, Wannier90, LAMMPS, Scikit-learn, TensorFlow, LightGBM.
- HPC clusters: PBS and SLURM.
- Examples: Notebooks and test scripts to explain the package.
- Available datasets: Summary of several datasets .
Please see Installation instructions
Please find several Google Colab Notebooks
>>> 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))
36099Please see Publications related to JARVIS-Tools
https://figshare.com/authors/Kamal_Choudhary/4445539
https://pypi.org/project/jarvis-tools
Please report bugs as Github issues (https://github.com/usnistgov/jarvis/issues) or email to [email protected].
NIST-MGI (https://www.nist.gov/mgi).