Accelerated Science
Machine learning can help accelerate materials science across a variety of climate change applications. Examples include:[1]
- The design of solar fuels, i.e., synthetic fuels produced from sunlight or solar heat.
- The design or characterization of battery conducting fluids.
- The design of alternatives to cement.
- The design of better CO2 sorbents.
Background Readings
- "Machine learning for molecular and materials science" (2018)[2]: A summary of "recent progress in machine learning for the chemical sciences" and proposed future directions for machine learning's use in the "design, synthesis, characterization and application of molecules and materials."
- "Materials discovery and design using machine learning" (2017)[3]: A review of machine learning for materials science.
Online Courses and Course Materials
Community
Libraries and Tools
Data
- The Materials Project: "[C]omputed information on known and predicted materials as well as powerful analysis tools to inspire and design novel materials." Available here.
- Inorganic Crystal Structure Database: "[T]he world's largest database for completely identified inorganic crystal structures." Available here.
- SciFinder: Chemical and materials science database (paid), available here.
- "Concrete Compressive Strength": Dataset of concrete compressive strength available here via the UCI Machine Learning Repository.
Future Directions
References
- ↑ "Tackling climate change in the EU". Climate Change and Law Collection.
- ↑ Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh; Isayev, Olexandr; Walsh, Aron (2018-07). "Machine learning for molecular and materials science". Nature. 559 (7715): 547–555. doi:10.1038/s41586-018-0337-2. ISSN 0028-0836. Check date values in:
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(help) - ↑ Slightam, Jonathon; Nagurka, Mark. "Machine Design Experiments Using Gears to Foster Discovery Learning". 2015 ASEE Annual Conference and Exposition Proceedings. ASEE Conferences. doi:10.18260/p.24438.