High throughput computation with density functional theory, molecular dynamics and machine learning. https://jarvis.nist.gov/
Project description
NIST-JARVIS
Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated framework for computational science using density functional theory, classical force-field/molecular dynamics and machine-learning. The jarvis-tools package can be used for high-throughput computation, data-analysis, and training machine-learning models. Some of the packages used in the jarvis-tools package are shown below. JARVIS-official website: https://jarvis.nist.gov
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.
References
- JARVIS-FF:
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
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:
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
Computational Screening of High-performance Optoelectronic Materials using OptB88vdW and TBmBJ Formalisms, Scientific Data 5, 180082 (2018).https://www.nature.com/articles/sdata201882
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
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
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
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
Data-driven Discovery of 3D and 2D Thermoelectric Materials , https://arxiv.org/abs/1903.06651.
- JARVIS-ML:
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
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 https://doi.org/10.1557/mrc.2019.95
External links
https://pypi.org/project/jarvis-tools
https://jarvis-tools.readthedocs.io/en/latest/
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