Accelerated Science: Difference between revisions
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Machine learning can help accelerate materials science |
Machine learning can help accelerate materials science across a variety of climate change applications. Examples include:<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> |
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* The design of solar fuels, i.e., synthetic fuels produced from sunlight or solar heat. |
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* The design or characterization of battery conducting fluids. |
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* The design of alternatives to cement. |
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* The design of better CO<sub>2</sub> sorbents. |
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== Background Readings == |
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* '''"Machine learning for molecular and materials science" (2018)'''<ref>{{Cite journal|last=Butler|first=Keith T.|last2=Davies|first2=Daniel W.|last3=Cartwright|first3=Hugh|last4=Isayev|first4=Olexandr|last5=Walsh|first5=Aron|date=2018-07|title=Machine learning for molecular and materials science|url=http://dx.doi.org/10.1038/s41586-018-0337-2|journal=Nature|volume=559|issue=7715|pages=547–555|doi=10.1038/s41586-018-0337-2|issn=0028-0836}}</ref>: 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." |
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* '''"Materials discovery and design using machine learning" (2017)'''<ref>{{Cite journal|last=Slightam|first=Jonathon|last2=Nagurka|first2=Mark|title=Machine Design Experiments Using Gears to Foster Discovery Learning|url=http://dx.doi.org/10.18260/p.24438|journal=2015 ASEE Annual Conference and Exposition Proceedings|publisher=ASEE Conferences|doi=10.18260/p.24438}}</ref>: A review of machine learning for materials science. |
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==Online Courses and Course Materials== |
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==Community== |
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==Libraries and Tools== |
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== Data == |
== Data == |
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*'''The Materials Project''': "[C]omputed information on known and predicted materials as well as powerful analysis tools to inspire and design novel materials." Available [https://materialsproject.org/ here]. |
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*'''Inorganic Crystal Structure Database''': "[T]he world's largest database for completely identified inorganic crystal structures." Available [https://icsd.products.fiz-karlsruhe.de/en/ here]. |
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*'''SciFinder''': Chemical and materials science database (paid), available [https://www.cas.org/products/scifinder here]. |
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*[https://archive.ics.uci.edu/ml/datasets/ |
*'''"Concrete Compressive Strength"''': Dataset of concrete compressive strength available [https://archive.ics.uci.edu/ml/datasets/ here] via the UCI Machine Learning Repository. |
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==Future Directions == |
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== References == |
== References == |
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Revision as of 19:20, 31 August 2020
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:
|date=(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.