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.
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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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wines1/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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