

[](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Upload_benchmark_to_jarvis_leaderboard.ipynb)
[]([https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Upload_benchmark_to_jarvis_leaderboard.ipynb](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/alignn_jarvis_leaderboard.ipynb))
[](https://pepy.tech/project/jarvis_leaderboard)
[](https://zenodo.org/badge/latestdoi/514340921)
# 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/
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