jarvis-tools 2020.5.13

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/).

• 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. Dataset will be made available and the website will be available soon.
  JARVIS-EFG	Electric field gradient dataset. Dataset will be made available and the website will be available soon.

Installing jarvis-tools

• 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 3.6 miniconda exe and install (for windows)
  Now, let's make a conda environment just for JARVIS::
  conda create --name my_jarvis python=3.6
  source activate my_jarvis

• Git clone install (Recommended):

  pip install numpy scipy matplotlib
  git clone https://github.com/usnistgov/jarvis.git
  cd jarvis
  python setup.py install

• Alternative pip install:

  pip install numpy scipy matplotlib
  pip install jarvis-tools

• Alternative nix install:: Nix allows a robust and reproducible package for Linux. To generate a Nix environment for using JARVIS, follow the Nix instructions.

Example Jupyter notebooks

Look into the notebooks folder

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

References

• JARVIS-FF:

  1. Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface, Nature: Sci Data. 4, 160125 (2017). https://www.nature.com/articles/sdata2016125

  2. High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields, J. Phys. Cond. Matt. 30, 395901(2018). http://iopscience.iop.org/article/10.1088/1361-648X/aadaff/meta

• JARVIS-DFT:

  3. High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory, Scientific Reports 7, 5179 (2017). https://www.nature.com/articles/s41598-017-05402-0

  4. Computational Screening of High-performance Optoelectronic Materials using OptB88vdW and TBmBJ Formalisms, Scientific Data 5, 180082 (2018). https://www.nature.com/articles/sdata201882

  5. Elastic properties of bulk and low-dimensional materials using van der Waals density functional, Phys. Rev. B, 98, 014107 (2018). https://journals.aps.org/prb/abstract/10.1103/PhysRevB.98.014107

  6. High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage, Nature: Sci. Rep. 9, 8534,(2019), https://www.nature.com/articles/s41598-019-45028-y

  7. Computational Search for Magnetic and Non-magnetic 2D Topological Materials using Unified Spin-orbit Spillage Screening, npj Comp. Mat., 6, 49 (2020). https://www.nature.com/articles/s41524-020-0319-4 .

• JARVIS-ML:

  8. Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape, Phys. Rev. Mat., 2, 083801 (2018). https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.2.083801

  9. Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations, Comp. Mat. Sci. 161, 300 (2019). https://www.sciencedirect.com/science/article/pii/S0927025619300813?via%3Dihub

  10. Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics, MRS Comm., 1-18, 2019. https://doi.org/10.1557/mrc.2019.95

  11. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning, Nature Comm., 10, 1, (2019). https://www.nature.com/articles/s41467-019-13297-w

  12. Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods, Chem. Mater., https://pubs.acs.org/doi/10.1021/acs.chemmater.9b02166

  13. High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses, https://arxiv.org/abs/1910.01183.

  14. Data-driven Discovery of 3D and 2D Thermoelectric Materials , https://arxiv.org/abs/1903.06651.

External links

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

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

https://www.slideshare.net/KAMALCHOUDHARY4

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

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).