Why Materials Science Can't Have an AlphaFold — And What It Takes to Change That
What if the AI revolution in science stalls outside biology's tidy amino acid playground? Prof. Heather Kulik reveals why materials discovery demands more than models— it craves dirty, real-world data.
⚡ Key Takeaways
- Materials lack biology's clean datasets— noisy DFT and sparse experiments hinder AlphaFold-style leaps.
- AI excels at novel polymer designs but needs lab synthesis to prove worth; quantum effects surprise experts.
- LLMs falter on precise chemistry tasks like 22-atom ligands, exposing domain gaps vs. biology.
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Originally reported by Latent Space
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