jarvis_leaderboard
Project description
JARVIS-Leaderboard
This project provides benchmark-performances of various methods for materials science applications using the datasets available in JARVIS-Tools databases. Some of the methods are: Artificial Intelligence (AI), Electronic Structure (ES), Force-field (FF), Qunatum Computation (QC) and Experiments (EXP). There are a variety of properties included in the benchmark. In addition to prediction results, we attempt to capture the underlyig software, hardware and instrumental frameworks to enhance reproducibility. This project is a part of the NIST-JARVIS infrastructure.
Website: https://pages.nist.gov/jarvis_leaderboard/
Quick start using GoogleColab notebook examples
- Analyzing_data_in_the_JARVIS_Leaderboard.ipynb
- Upload_benchmark_to_jarvis_leaderboard.ipynb
- alignn_jarvis_leaderboard.ipynb
- kgcnn_jarvis_leaderboard.ipynb
- MatMiner_on_JARVIS_DFT.ipynb
- AtomVision_Image_Classification.ipynb
Directory tree structure preview
├── jarvis_leaderboard
│ ├── dataset
│ │ ├── AI
│ │ │ ├── ImageClass
│ │ │ │ └── stem_2d_image_bravais_class.json.zip
│ │ │ ├── MLFF
│ │ │ │ ├── alignn_ff_db_energy.json.zip
│ │ │ │ └── prepare.py
│ │ │ ├── SinglePropertyClass
│ │ │ │ ├── dft_3d_magmom_oszicar.json.zip
│ │ │ │ └── ...
│ │ │ ├── SinglePropertyPrediction
│ │ │ │ ├── dft_3d_exfoliation_energy.json.zip
│ │ │ │ ├── dft_3d_formation_energy_peratom.json.zip
│ │ │ │ ├── ...
│ │ │ └── TextClass
│ │ │ ├── arXiv_categories.json.zip
│ │ │ └── pubchem_categories.json.zip
│ │ ├── ES
│ │ │ ├── SinglePropertyPrediction
│ │ │ │ ├── dft_3d_Tc_supercon_JVASP_1151_MgB2.json.zip
│ │ │ │ ├── ...
│ │ │ │ └── prepare.py
│ │ │ └── Spectra
│ │ │ ├── dft_3d_dielectric_function.json.zip
│ │ │ ├── ...
│ │ ├── EXP
│ │ │ └── Spectra
│ │ │ └── dft_3d_XRD_JVASP_19821_MgB2.json.zip
│ │ ├── FF
│ │ │ └── SinglePropertyPrediction
│ │ │ └── dft_3d_bulk_modulus_JVASP_867_Cu.json.zip
│ │ └── QC
│ │ └── EigenSolver
│ │ └── dft_3d_electron_bands_JVASP_816_Al_WTBH.json.zip
│ ├── benchmarks
│ │ ├── alignn_model
│ │ │ ├── AI-SinglePropertyPrediction-exfoliation_energy-dft_3d-test-mae.csv.zip
│ │ │ ├── AI-SinglePropertyPrediction-formation_energy_peratom-dft_3d-test-mae.csv.zip
│ │ │ ├── AI-Spectra-ph_dos-dft_3d-test-multimae.csv.zip
│ │ │ ├── ...
│ │ │ ├── metadata.json
│ │ │ ├── run.py
│ │ │ ├── run.sh
│ │ ├── densenet_model
│ │ │ ├── AI-ImageClass-bravais_class-stem_2d_image-test-acc.csv.zip
│ │ │ ├── metadata.json
│ │ │ └── run.sh
│ │ ├── qe_pbesol_gbrv
│ │ │ ├── ES-SinglePropertyPrediction-Tc_supercon_JVASP_1151_MgB2-dft_3d-test-mae.csv.zip
│ │ │ └── metadata.json
│ │ │ └── run.sh
│ │ ├── qiskit_vqd_SU2
│ │ │ ├── QC-EigenSolver-electron_bands_JVASP_816_Al_WTBH-dft_3d-test-multimae.csv.zip
│ │ │ └── metadata.json
│ │ ├── qmcpack_dmc_pbe
│ │ │ ├── ES-SinglePropertyPrediction-bandgap_JVASP_1002_Si-dft_3d-test-mae.csv.zip
│ │ │ ├── ...
│ │ │ ├── metadata.json
│ │ │ ├── run.py
│ │ │ └── run.sh
│ │ │ └── run.sh
│ │ ├── ...
│ │ │ ├── ...
│ │ │ ├── ...
├── docs
│ ├── AI
│ │ ├── ImageClass
│ │ │ ├── bravais_class.md
│ │ │ └── index.md
│ │ ├── MLFF
│ │ │ ├── energy.md
│ │ │ └── index.md
│ │ ├── SinglePropertyClass
│ │ │ ├── index.md
│ │ │ └── ...
│ │ ├── SinglePropertyPrediction
│ │ │ ├── Band_gap_HSE.md
│ │ │ ├── exfoliation_energy.md
│ │ │ ├── formation_energy_peratom.md
│ │ │ ├── optb88vdw_bandgap.md
│ │ │ └── ...
│ │ ├── Spectra
│ │ │ ├── index.md
│ │ │ └── ph_dos.md
│ │ ├── TextClass
│ │ │ ├── categories.md
│ │ │ └── index.md
│ │ └── index.md
│ ├── ES
│ │ ├── SinglePropertyPrediction
│ │ │ ├── Tc_supercon_JVASP_1151_MgB2.md
│ │ │ ├── Tc_supercon_JVASP_11981_Nb3Al.md
│ │ │ └── ...
│ ├── populate_data.py
│ ├── rebuild.py
│ └── scripts
│ ├── convert.py
│ ├── format_data.py
│ └── transform.py
├── mkdocs.yml
├── requirements.txt
└── setup.py
Citation
@article{choudhary2020joint,
title={The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design},
author={Choudhary, Kamal and Garrity, Kevin F and Reid, Andrew CE and DeCost, Brian and Biacchi, Adam J and Hight Walker, Angela R and Trautt, Zachary and Hattrick-Simpers, Jason and Kusne, A Gilad and Centrone, Andrea and others},
journal={npj computational materials},
volume={6},
number={1},
pages={173},
year={2020},
publisher={Nature Publishing Group UK London}
}
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