cseeg/DiSCoVeR-SuperCon-NOMAD-SMACT
Composition-based predictions for chemically novel, high-temperature superconductors.
This tool helps materials scientists and researchers identify new, high-temperature superconducting materials. It takes existing superconductor data and a list of chemical formulas, then predicts novel compositions with high superconducting critical temperatures. The output is a ranked list of promising chemical formulas, indicating their predicted performance and chemical novelty.
No commits in the last 6 months.
Use this if you are a materials scientist or chemist looking to accelerate the discovery of new high-temperature superconductors by exploring chemically novel compositions.
Not ideal if you need to synthesize materials directly or perform detailed quantum mechanical simulations, as this tool focuses on predicting promising candidates.
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9
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2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 07, 2023
Commits (30d)
0
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