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This project provides benchmark-performances of various methods for scientific applications using the datasets available in JARVIS-Tools databases.

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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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This project provides benchmark-performances of various methods for scientific applications using the datasets available in JARVIS-Tools databases.

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  • Python 66.2%
  • Jupyter Notebook 31.9%
  • Shell 1.9%