Accelerated Science: Difference between revisions
→Background Readings
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Machine learning can help accelerate materials science across a variety of climate change applications by learning patterns in experimental or operational data in order to guide future experiments/operations. Examples include:<ref>{{Cite journal|last=Rolnick|first=David|last2=Donti|first2=Priya L.|last3=Kaack|first3=Lynn H.|last4=Kochanski|first4=Kelly|last5=Lacoste|first5=Alexandre|last6=Sankaran|first6=Kris|last7=Ross|first7=Andrew Slavin|last8=Milojevic-Dupont|first8=Nikola|last9=Jaques|first9=Natasha|last10=Waldman-Brown|first10=Anna|last11=Luccioni|first11=Alexandra|date=2019-11-05|title=Tackling Climate Change with Machine Learning|url=http://arxiv.org/abs/1906.05433|journal=arXiv:1906.05433 [cs, stat]}}</ref>
* 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 CO<sub>2</sub> sorbents, i.e., new chemical sorbents that either bind to atmospheric CO<sub>2</sub> with either greater selectivity or have lower energy input requirements.
== Background Readings ==
* '''"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."
* '''"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.
* '''"Paving the wall for Low-Carbon Concrete"''' (2020)<ref>Talati, Shuchi; Merchant, Na'im; Neidl, Chris. (2020-12). [https://static1.squarespace.com/static/5b9362d89d5abb8c51d474f8/t/5fd95907de113c3cc0f144af/1608079634052/Paving+the+Way+for+Low-Carbon+Concrete "Paving the Way for Low Carbon Concrete]". Carbon180 white paper.</ref>: A white paper on how cement contributes to carbon emissions and what can be done about it.
==Online Courses and Course Materials==
==Conferences, Journals, and Professional Organizations==
==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 [https://materialsproject.org/ here].
*'''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].
*'''SciFinder''': Chemical and materials science database (paid), available [https://www.cas.org/products/scifinder here].
*'''"Concrete Compressive Strength"''': Dataset of concrete compressive strength available [https://archive.ics.uci.edu/ml/datasets/ here] via the UCI Machine Learning Repository.
*'''Open Catalyst Project:''' Dataset of 1.2 million molecular relaxations with results from over 250 million DFT calculations, aimed towards the discovery of new catalysts for use in renewable energy storage. Available [https://opencatalystproject.org/index.html here].
== Relevant Groups and Organizations ==
==Future Directions ==
== References ==
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