Accelerated Science: Difference between revisions
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Revision as of 18:50, 31 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.
References
- ↑ "Tackling climate change in the EU". Climate Change and Law Collection.