Accelerated Science: Difference between revisions
Content added Content deleted
m (Priya moved page Accelerated Materials Science Datasets to Accelerated Materials Science over a redirect without leaving a redirect: make page more general than datasets) |
|||
| (8 intermediate revisions by one other user not shown) | |||
| Line 1: | Line 1: | ||
{{Stub}} |
|||
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. |
|||
{{Disclaimer}} |
|||
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 == |
== 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]. |
||
*[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. |
||
*'''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 == |
== References == |
||
Latest revision as of 20:53, 22 December 2020
🌎 This article is a stub, and is currently under construction. You can help by adding to it!
This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.
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:[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, i.e., new chemical sorbents that either bind to atmospheric CO2 with either greater selectivity or have lower energy input requirements.
Background Readings[edit | edit source]
- "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.
- "Paving the wall for Low-Carbon Concrete" (2020)[4]: A white paper on how cement contributes to carbon emissions and what can be done about it.
Online Courses and Course Materials[edit | edit source]
Conferences, Journals, and Professional Organizations[edit | edit source]
Libraries and Tools[edit | edit source]
Data[edit | edit source]
- 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.
- 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 here.
Relevant Groups and Organizations[edit | edit source]
Future Directions[edit | edit source]
References[edit | edit source]
- ↑ Rolnick, David; Donti, Priya L.; Kaack, Lynn H.; Kochanski, Kelly; Lacoste, Alexandre; Sankaran, Kris; Ross, Andrew Slavin; Milojevic-Dupont, Nikola; Jaques, Natasha; Waldman-Brown, Anna; Luccioni, Alexandra (2019-11-05). "Tackling Climate Change with Machine Learning". arXiv:1906.05433 [cs, stat].
- ↑ 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.
- ↑ Talati, Shuchi; Merchant, Na'im; Neidl, Chris. (2020-12). "Paving the Way for Low Carbon Concrete". Carbon180 white paper.