Can AI revolutionize materials discovery?

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Sep 19, 2024

Ekin Dogus Cubuk, a research scientist at Google DeepMind, specializes in AI applications for materials discovery. He discusses the complexities of using AI in finding new materials for climate tech, highlighting that current methods still require substantial lab work and larger datasets. The conversation also explores how AI might enhance the understanding of material properties like optical and electric qualities, and the challenges faced due to inconsistent data. Dogus emphasizes the need for quality experimental data to make AI truly impactful in this field.

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ANECDOTE

Accidental Discoveries

Early materials discoveries were often serendipitous, like tungsten's increased ductility after accidental mercury exposure.
Similarly, lithium-ion batteries were discovered while researching superconductors at Exxon.

ANECDOTE

Bardeen's Transistor

Bardeen's transistor development involved trial and error with various materials, highlighting the role of experimentation.
Despite his expertise, he had to experiment to find what worked.

INSIGHT

Optimization vs. Discovery

Optimizing existing materials is easier than discovering new ones, leading to incremental improvements.
This explains why many current technologies use relatively simple, long-known materials like silicon.

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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, or Dogus, 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

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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

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