Periodic Labs co-founder Doge Chubuk discusses why reasoning models like OpenAI o1 changed his conviction on AI for materials discovery.
Insight
Chubuk: AI cannot reason to breakthrough superconductors from training data alone
“I think it's still true that it would be difficult to just reason your way into a much better superconductor. I actually would guess that there's a law out there that we haven't discovered yet that says that you can't just look at your training set that's diff…”
Insight
Chubuk: LLMs already bridge solid-state chemistry and physics better than human specialists
“Like, there was probably a time when a physicist could contribute and be one of the best in the world on many fields of physics, but it's definitely not true today, and this is one of the reasons I think we are very excited about LLMs, because when you talk to…”
Assertion Not checkable as stated
Chubuk: AI is currently not better than humans at hypothesis generation
“It does seem like today there are things that ML, AI is better than humans, but one of those things is not hypothesis generation.”
Disclosure
Chubuk: GPU compute and training costs drove Periodic Labs' $300M seed
“We are going to train LLMs, we are going to use GPUs to run simulations, so that does end up being a large part of the cost. Yeah, it's funny, like, before, you know, if you asked me this question 10 years ago, I would have thought that the biggest part of the…”
Insight
Chubuk: Science requires out-of-domain generalization unlike standard ML
“Machine learning works best on the training set distribution. But in science and technology, we almost only care about auto-domain generalization, right?”
Assertion Not checkable as stated
Chubuk: High-throughput liquid- and powder-mixing robots have become commoditized
“These robots, they became quite commoditized, actually, just mixing powders, or mixing liquids, and then sending it to characterization.”