jarvis-tools: an open-source software package for data-driven atomistic materials design. https://jarvis.nist.gov/
Project description
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/).
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
Example Jupyter notebooks
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
External links
https://figshare.com/authors/Kamal_Choudhary/4445539
https://pypi.org/project/jarvis-tools
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).
Project details
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