Accelerated Science

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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

  • "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

Conferences, Journals, and Professional Organizations

Libraries and Tools


  • 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

Future Directions


  1. 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].
  2. 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)
  3. 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.
  4. Talati, Shuchi; Merchant, Na'im; Neidl, Chris. (2020-12). "Paving the Way for Low Carbon Concrete". Carbon180 white paper.