jarvis-tools 2020.7.8

jarvis-tools: an open-source software package for data-driven atomistic materials design. https://jarvis.nist.gov/

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JARVIS-Tools: an open-source software package for data-driven atomistic materials design

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

• A summary of the projects

  Projects	Brief description
  JARVIS-DFT	Density functional theory calculation database for ~40000 3D and ~1000 2D materials. Some of the material-properties include: Heat of formation, Crystal-structural data using OptB88vdW, PBE, LDA functionals, Bandgaps using semi-local, meta-GGA, HSE06 and other beyond DFT methods, Electron and phonon-bandstructures, Elastic, Piezoelectric, Thermoelectric, Dielectric tensors, Exfoliation energies for low-diemnsional materials, Frequency dependent dielectric function, Absorption coefficients, Work-function for 2D materials, Infrared and Raman intensities, Electric field gradient, Magnetic moment, Solar-cell efficiencies, Scanning Tunneling Microscopy (STM) images, Topological spin-orbit spillage, converged k-point and plane wave cut-offs, Wannier-tight binding Hamiltonian parameters and more. The website for JARVIS-DFT: https://www.ctcms.nist.gov/~knc6/JVASP.html
  JARVIS-FF	Classical molecular dynamics calculation database for ~2000 3D materials with interatomic potential/force-fields. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials. The website for JARVIS-FF: https://www.ctcms.nist.gov/~knc6/periodic.html
  JARVIS-ML	Machine learning prediction tools trained on the JARVIS-DFT data. Some of the ML-prediction models are for Heat of formation, GGA/METAGGA bandgaps, Refractive indices, Bulk and shear modulus, Magnetic moment, Thermoelectric, Piezoelectric and Dielectric properties properties, Exfoliation energies, Solar-cell efficiency, and STM image classification. The website for JARVIS-ML: https://www.ctcms.nist.gov/jarvisml/
  JARVIS-Het.	Heterostructure design tools for 2D materials in the JARVIS-DFT database. Some of the properties available are: work function, Band-alignment, and Heterostructure classification. JARVIS-Heterostructure website: https://www.ctcms.nist.gov/jarvish/
  JARVIS-PV	Solar-cell/Photovoltaic cell design tools. Dataset is made available and the website will be available soon.
  JARVIS-STM	Scanning-tunneling microscopy images for 2D materials. Dataset is made available and the website will be available soon.
  JARVIS-WTB	Wannier Tight Binding Hamiltonian parameter dataset. Website: https://www.ctcms.nist.gov/jarviswtb .
  JARVIS-EFG	Electric field gradient dataset. Dataset will be made available and the website will be available soon.
  Downloads	Download raw metadat at: https://www.ctcms.nist.gov/~knc6/downloads.html

Installation

Please see Installation instructions

Example Jupyter notebooks

Please find several Google Colab Notebooks

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))
36099

References

Please see Publications related to JARVIS-Tools

External links

https://figshare.com/authors/Kamal_Choudhary/4445539

https://pypi.org/project/jarvis-tools

https://jarvis-tools.readthedocs.io/en/latest/

https://www.slideshare.net/KAMALCHOUDHARY4

Correspondence

Please report bugs as Github issues (https://github.com/usnistgov/jarvis/issues) or email to [e-mail].

Funding support

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