I am in computational chemistry which has some crossover with Materials Science. AI is useful for speeding up simulations, doing molecular interaction predictions, doing property predictions, doing molecular docking. LLMs are helpful as research assistants and helping design and run experiments. https://atomictessellator.com/

For example, my latest module (I am putting the finishing touches on this as we speak) uses LLMs to review catalysis literature and then summarize that and control another coding LLM that has been trained to run the simulation tools I created to try and reproduce the works in the papers. Yes, it works, the first catalyst discoveries were made just a few days ago.

Kind of mind blowing. A light in the tunnel for the reproduction crisis?

What specifically have you trained your coding LLM on? Is it lora or something more advanced? Have you created a corpus by hand specifically for training?

Yes, created by hand, lots of techniques are required to get a well running system, GoT (graph of thought) RAG (retrieval augmented generation), Ko detection, dynamic problem decomposition, and a few more techniques I have invented but dont really have names for. Its also quite a bit more complicated than the simplistic answer I gave before because you have to do things like experiments in abstraction laddering to get good interface composability.

This space is moving so quick I run many experiments every day.

I would love to work on this full time, I applied to y-combinator but I didn't get in :(

That’s too bad, did YC give you a reason?

YC doesn't give reasons.

Random question for you: How feasible is it for someone with a software/ML background to get into this space? I'd like to get into learning a new domain through ML, but it feels very daunting.

It sounds cliche but the old advice “do something you’re passionate about” applies. I have loved chemistry since I was a teenager because I had an inspiring teacher, without this passion I would have given up when I got to the very hard parts of math.

Personally I had to level up my math game a lot, when I started I had to keep pausing looking up terms, then write tiny prototypes, then keep learning. At the start it took me a month to read and understand a paper, now I can skim read them in minutes, without a passion for chemistry and the self discipline to keep at it I would have given up.

Take extensive notes, actually review them frequently, and refractor them as you learn.

Find mentors in the space, while reading papers I took names and reached out, I offered free programming to anyone who I found inspiring, and this built relationships with some pretty amazing professors and students around the world.

Hope that helps! <3

Fwiw there are other angel/seed/vc investors specializing in materials science you might apply to:

https://signal.nfx.com/investor-lists/top-material-science-s...

https://www.businesswire.com/news/home/20210412005180/en/TDK...

https://www.wsj.com/articles/bill-gates-backs-new-vc-fund-fo...

Are you reproducing the results of other simulation papers by running simulations, or results from other experimental papers?

Is interpreting the other papers and translating them into simulation parameters a rate limiting step in catalysis research? Or is this like, fitting your simulation package’s parameters to get the same output as someone else’s?

Previously worked on the intersection of ML and Material Science (specifically batteries). I think the link to osium-ai[0] tells the story pretty well. Material science is plagued by long, expensive, exploration phases - even in places you wouldn't expect them. ML ends up being really good at cutting the expense of these phases by >50% by making you just a little smarter in your exploration.

[0] https://www.ycombinator.com/companies/osium-ai

But this isn't new, isn't it?

I remember, e.g, Italy and Brazil had projects more than ten years ago where they used some sort of machine learning to find hidden historical buildings under terrain or jungle by looking at patterns in satellite/aerial image and it was successful in finding archeological sites in both countries.

By “exploration” he’s referring to Materials Science stuff, like figuring out molecular combinations that produce materials with desired properties. Not Archeological/GIS exploration.

The examples of Materials Science and Archeology are both looking for patterns.

Very similar in nature yes - we had a few rather spectacular results using only techniques from the 80s and earlier.

A lot of the revolution is coming from material scientists doing the work of creating data sets that encode their knowledge, better computational techniques in the field (e.g. DFT[0]), and democratization of petabyte scale data pipelines.

[0] https://en.wikipedia.org/wiki/Density_functional_theory