Accelerated Science: Difference between revisions
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Machine learning can help accelerate materials science for climate change applications such as the design of solar fuels, battery conducting fluids, alternatives to cement, or better CO<sub>2</sub> sorbents.<ref>{{Cite web|title=Tackling climate change in the EU|url=http://dx.doi.org/10.1163/9789004322714_cclc_2017-0189-005|website=Climate Change and Law Collection}}</ref> Some potentially relevant datasets are listed below. |
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== Data == |
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===Accelerated science for materials=== |
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*[https://materialsproject.org/ The Materials Project] |
*[https://materialsproject.org/ '''The Materials Project''']: "[C]omputed information on known and predicted materials as well as powerful analysis tools to inspire and design novel materials." |
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*'''[https://icsd.products.fiz-karlsruhe.de/en/ Inorganic Crystal Structure Database]''': "[T]he world's largest database for completely identified inorganic crystal structures." |
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*[https://www.cas.org/products/scifinder SciFinder] (paid) |
*[https://www.cas.org/products/scifinder '''SciFinder''']: Chemical and materials science database (paid). |
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*[https://archive.ics.uci.edu/ml/datasets/ UCI Machine Learning Repository |
*[https://archive.ics.uci.edu/ml/datasets/ '''"Concrete Compressive Strength" from the UCI Machine Learning Repository''']: Dataset of concrete compressive strength. |
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Revision as of 03:57, 28 August 2020
Machine learning can help accelerate materials science for climate change applications such as the design of solar fuels, battery conducting fluids, alternatives to cement, or better CO2 sorbents.[1] Some potentially relevant datasets are listed below.
Data
- The Materials Project: "[C]omputed information on known and predicted materials as well as powerful analysis tools to inspire and design novel materials."
- Inorganic Crystal Structure Database: "[T]he world's largest database for completely identified inorganic crystal structures."
- SciFinder: Chemical and materials science database (paid).
- "Concrete Compressive Strength" from the UCI Machine Learning Repository: Dataset of concrete compressive strength.
- ↑ "Tackling climate change in the EU". Climate Change and Law Collection.