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I think, of course, skepticism around this "LLM discovers X" thing is warranted, and there have been plenty of more recent examples around questionable LLM "discoveries". Just stating this because the LK99 thing I believe was notable as a (supposed) room-temp _super_conductor while this is about a _semi_conductor.

I start my day with plenty of optimism, then I go back and forth in the CLI and find out most of whats posted online is fake, and then towards the end of the day 2h past my bed time I end up ed zitron maxxing, it is the way it is ig

> After the LK-99 debacle

"Debacle"? That was the most fun I've had on the Internet in years. When's the last time so many people engaged in so many arguments about materials science and electromagnetism? Sometime in the 1800s?

Yeah I remember going to my physics professor super excited about LK-99 to ask him if he heard about it, and him just telling me "yes but stuff like that happens twice per year, they will find something is off", and in fact it's what happened...

You probably meant "I'm taking this with a tiny pinch of salt". The amount of salt is directly proportional to how much of the claim you are willing to accept.

Edit: I stand corrected. According to Gemini:

Me: Does using more salt mean accepting more of that claim?

Gemini: No, it actually means the exact opposite. If you say you need to take a claim with a huge pile of salt (or a shovel of salt), it means you believe the claim is highly unbelievable and you need an immense amount of skepticism to accept it. How the Metaphor Scales

• A single grain of salt: "I am slightly skeptical, but it could be true."

• A pinch of salt: "I have a healthy amount of doubt about this."

• A grain of sand / A truckload of salt: "This sounds completely made up, and I barely believe a single word of it."

The salt represents your skepticism, not your belief. Therefore, the more unbelievable the claim, the more "salt" you need to swallow it.

Anecdata: over the weekend, on a whim, I decided to download a real fly’s brain’s weights [0], run it on a simulated task like finding food, then train a logistic classifier using the fly’s decisions as the expert, then use the trained classifier as a decision model to simulate the fly on a 3d environment, running in real time on a website

It took me (using Claude code and some codex), about 3 hours to put it together

And even though it was a cool demo, it seemed so easy, that it also felt like it wasn’t worth sharing

0: ChessFly (not mine), uses the FlyWire connectome (the fly’s brain’s weights) to play chess https://huggingface.co/spaces/mlabonne/chessfly

It is not at all obvious that merely because we have words for concepts, that a model should be able to do all these miraculous mathematical and scientific things.

Yeah...?

That's the pitch of LLMs lol

This is what happens when Claude writes it for you (and you don't review it)

I also like how they explained ferromagnetism as being arranged atomic magnets. Magnets all the way down.

A lot of the public successes with agents is really LLM-driven local search against an objective function that is evaluated in more traditional ways. This one seems to fit the pattern.

From the little I understand about this topic, it looks similar to approaches used in the recent Navier-Stokes breakthrough. These physical systems are governed by partial differential equations (PDEs) which can be solved numerically using standard algorithms. So when we say "simulation" in this context we really just mean "numerical solution".

In the case of quantum mechanics, it's the Schrödinger equation, which is no different than any other PDE. Agents are getting very good at searching through the space of possible simulation parameters and initial conditions to find solutions with certain properties. Coarser simulations are less accurate but faster to run, so the search uses simulations at different scales to find promising directions, and then refines those to verify that the simulation converges on the expected result.

One of the potential applications of quantum computing is that it might speed these simulations up exponentially. But scientists can and do regularly simulate quantum systems on classical computers.

I'm under the impression that this kind of modeling is one of the applications that quantum computers are likely to be good at.

I'd imagine there's a lot of documented research which has attempted to find such things using classical computers.

Seems like there would be a lot of well structured context for somebody to use while directing agents to repeat that research, now with updated models once quantum computing is ready for that kind of task.

not a classic simluation- a quantum simulation. This means they put a lot more work into representing the wave function of the simulation and modelling quantum effects.

They ran Quantum Espresso which is ok, but by no means the 'state of the art' for DFT. And in case, any DFT computation has to be taken with a few pounds of grains of salt before getting too excited about it.

No offense to the person writing this (assuming they did at all), but I'm not sure they really understand what they're doing..

Frankly, there is no point in trying to "understand" what an LLM does. Their thought process is effectively undecipherable by humans (it's essentially information arising from information) so even such a "simple explanation" is almost certainly wrong. The agents might appear to have "used this method", but the actual method of computation is far beyond our grasp.

Why are people being so belligerent about this? I thought it's fairly obvious at this point that LLM reasoning is far beyond anyones understanding. Or does anyone have a refutation?

One of the materials is most likely impossible to synthesize. The other already exists, so that may actually be capable of being tested. It's only been synthesized once, 27 years ago though.

Which is somewhat ironic since neural networks were "discovered" back in the 1940s... then forgotten... then wait, they were discovered again! ... then forgotten, again... and now here we are.

Well its not just any old human doing these things in a general sense. Its typically academics or highly paid researchers who love doing work like this. So, I don't think it will just one day stop

Yes, as long as we are advancing to behavior and world models, so that agents can interact with the world themselves. Which we are.

This is frankly one of the best uses of LLMs (along with proposing and evaluating drug therapies), and I think it's (at least partially) because these are things that will only work in the hands of people who are already experts and motivated in the field. The proposed thing is validate (or not validated), and then everyone moves on (either using the cool new thing, or knowing that it doesn't work). I'd also throw robotics in here.

The fact that the major "uses" of LLMs have been contributing to the acceleration of the dead internet theory, and building millions of versions of the same apps that no one is going to maintain, is extremely sad.

All research is built off the existing body of all research data done by other people

I would image it's the data the researchers fed the agents and in which a discovery was likely. Especially since it's "candidates", so it's not like a proper discovery.

This gave me the idea to actually create a full (QED accurate) atomic simulation software. Essentially would allow you to play around with things like this. At a glance my workstation _probably_ has enough compute to handle it. At least to fully simulate at least a few dozen atoms and compounds.

This is semiconductors, not superconductors.

That was a room temperature superconductor, a bit different of a task.

Well, given that this is a semiconductor, and not a superconductor, I don't see how that is relevant?

I agree that it is about as ground breaking as "Yet another promising nuclear fusion candidate theoretically described."

I'm not sure why you would consider new and promising avenues for research to not be ground breaking. If it's an idea worth trying, it's an idea worth trying. If it doesn't survive testing, then it was still worth trying.

You are vastly overestimating what has been achieved here.

This is something a couple of materials science grad students can do in limited time for poor compensation as well. The expensive budget is for the part that comes next.