Accelerated Science

Machine learning can help accelerate materials science across a variety of climate change applications. Examples include:

• 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): 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): A review of machine learning for materials science.

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.