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Can AI revolutionize materials discovery?

Google DeepMind’s Ekin Dogus Cubuk says AI may be more useful for optimizing existing materials than for discovering new ones.

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Published
September 19, 2024
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Catalyst
Catalyst

AI is working its way across climate tech, helping companies discover giant lodes of ore, catch battery defects, and monitor energy infrastructure. Could it help us find revolutionary new materials, too?

Turns out, it’s complicated. 

In this episode, Shayle talks to Ekin Dogus Cubuk, a researcher focused on materials at Google DeepMind. DeepMind is one of several players, including Microsoft, trying to discover new materials that could be used in things like better battery chemistries, powerful carbon-capture sorbents, and room-temperature superconductors. But so far, Dogus says AI-powered approaches haven’t actually yielded any commercially-deployable materials.

Shayle and Dogus cover topics like:

  • Existing approaches to materials discovery, like experimentation and density functional theory, and how AI could complement those techniques
  • Why AI may actually require a lot more lab work — and larger datasets — before it becomes useful for material discovery
  • The types of material properties that AI may be especially useful for, such as optical or electric qualities

Recommended resources

  • Latitude Media: Armed with AI, Microsoft found a new battery material in just two weeks
  • Google DeepMind: Millions of new materials discovered with deep learning
  • Latitude Media: Could AI-powered defect detection boost battery manufacturing?

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