Just for clarity, the linked paper in the twitter thread is "An autonomous laboratory for the accelerated synthesis of novel materials" (<a href="https://www.nature.com/articles/s41586-023-06734-w" rel="nofollow noreferrer">https://www.nature.com/articles/s41586-023-06734-w</a>) which does have two authors from DeepMind but seems to be mostly from material science researchers at UC Berkeley. This thread is not about the recent Nature paper "Scaling deep learning for materials discovery" (<a href="https://www.nature.com/articles/s41586-023-06735-9" rel="nofollow noreferrer">https://www.nature.com/articles/s41586-023-06735-9</a>) from Deepmind which made news a few days ago.
The linked paper (<a href="https://www.nature.com/articles/s41586-023-06734-w" rel="nofollow noreferrer">https://www.nature.com/articles/s41586-023-06734-w</a>) in the quoted tweet appears to be from the Ceder Group at UC Berkeley, not DeepMind. Is there a different link I'm missing?
It appears that the OP misread the tweet. This has nothing to do with DeepMind.<p>For reference (in case the title gets changed), this post is currently titled "Chemist suggests retraction of DeepMind robotic synthesis paper".
I didn't see him explicitly suggest retraction in that thread, although he's certainly very upset at how bad the study is and thinks it should never have been published.
A somewhat related question: let's assume that a super duper ChemGPT has discovered new, heretofore unknown, molecules.<p>What happens next?<p>Is there lots of work still to be done before the molecules are "built"? How would a lab determine the properties of truly novel compounds? How would you even figure out how to synthesize a new molecule?
i work alongside this group ( one of Ceders PhD students), but not on this project. I believe OP is referencing a different paper to that of the tweet author