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.
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.
This is what you sound like. I also like how the goalposts keep moving on a daily basis, a year ago it was that LLMs can't even write a Hello World program without making an error, but now things like this are "so easy a minimum wage intern could do it."
You could characterise perceiving a fact to be true when it is not as a hallucination.
An idea is not a fact, Frodo Baggins is not a hallucination, but an idea. Believing that Frodo Baggins exists in our world could be considered a hallucination.
Newtons Laws of motion are not hallucinations even though the universe does not run on Newtonian physics. If I said that he told me about them this morning, that would be claiming a fact, not expressing an idea. That would likely be a hallucination.
This is a very bizarre introduction. People encounter diamagnets (e.g., copper) and paramagnets (e.g., aluminum) way more than they encounter antiferromagnets. I don't know why you'd ever cast magnetism as a false binary between ferromagnets and antiferromagnets, without acknowledging any other types of magnetic order.
(I did a PhD in magnetic materials)
Edit: I'll add that whether an antiferromagnet is useful, say, for exchange biasing a ferromagnetic thin film, depends on many factors. Just looking at antiferromagnetism alone you've got collinear vs non-collinear, G-type vs A-type vs C-type, commensurate vs incommensurate, and isotropic vs anisotropic; and all of that interacts with the interface structure, yada yada yada. It would be helpful if the authors elaborated on the expected properties of these materials. I personally don't know what people want room-temperature magnetic semiconductors for, but I'd be curious to learn what set of properties they think would be useful.
> The common one, the fridge magnet, is ferromagnetic — its atomic magnets all point the same way (up or down), adding their magnetic effects.
Ferromagnets typically have domains with magnetic moments that point in different directions. Ferromagnets rarely have every 'atomic magnet' pointing the same way.
https://en.wikipedia.org/wiki/Magnetic_domain
Refrigerator magnets in particular are usually magnetized as Halbach arrays, where the whole point is that the 'atomic magnets' are not pointing in the same direction. This is more energetically stable, which allows you to use cheaper materials.
https://en.wikipedia.org/wiki/Refrigerator_magnet
Lastly, I believe most refrigerator magnets are actually ferrimagnetic, not ferromagnetic. (The distinction doesn't matter much for users of magnets, but is important for the materials scientists studying and designing them.)
I don't know what I'm talking about, but it vaguely sounds like something that could make a small computer do more stuff, where heat is a big limiting factor in computer components today, and magnetism being a central component in many parts like storage
Thus, you can build ai/ml models+agents to explore those spaces, at a speed and scope much larger than what any human can do
I can imagine findings like these are going to keep increasing in frequency to a point in which the bar for novelty goes a lot higher
The agents ran quantum-mechanical simulations of each crystal with the standard method for this, density functional theory, at two levels of approximation: a faster one (PBE+U) and a slower, usually more accurate one (HSE06). The band gaps and spin windows below come from the more accurate one.
I don't see any claims that this is better than the current silicon and gallium arsenide semiconductors that we use. And the use of "room temperature" seems a deliberate attempt to misconstrue this with superconductors
2) it is specifically saying it is a magnetic semiconductor. The Wikipedia article on the topic says “ To date, GaMnAs remains the only semiconductor material with robust coexistence of ferromagnetism persisting up to rather high Curie temperatures around 100–200 K.” , so this would be something new. (The silicon chips in your smartphone are not ferromagnetic.)
If ai becomes so prolific that we humans all stop doing those things then will they still work?
https://www.technologyreview.com/2020/11/03/1011616/ai-godfa...
Next gen of scientists might look quite different.
Are they a promoter / influencer for Anthropic?
Is this a "actual impossible because it's inherently contradictory", or "we just don't know how to do it yet but give us a year"?
Many more things will be like this. The massive amounts of 'genius' buried under corporate management and obscurity in the past 500 years will be a treasure trove.
I can think of worse uses of VC AI funding.
Actual title:
Two Room-Temperature Antiferromagnetic Semiconductor Candidates
There's nothing unusual about finding room temperature semiconductors. I assume whoever posted it misread this as room temperature superconductors, but it has nothing to do with that.
What's interesting here is the antiferromagnetic part of the title, which was removed. I think this makes it relevant for e.g. RAM, but not superconducting. Someone can correct me if I'm wrong.
Not sure why we're calling it a discovery, when they've literally been made before, by a human.
It didn't discover anything. This is how cooked people are.
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.
"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?
The system worked as designed and as intended.
That was one of the best examples of science working nearly perfectly. One of the rare times I felt ok being human
I think as far as the 'debacle', there was definitely a lot of hype (at least as far as HN goes) around it, the level of buzz felt similar to what one would see today around a new AI model release.
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.
(+) Or a "grain" if you're from the US since American English sayings seem to all date from the middle ages, while the rest of the English speaking world tends to update ours over time. No shade meant, I've just always found that interesting.
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
I have no idea about those actual models, but there are layers of models that you can create, and for each, you can explore with data and compute
It's not a free lunch though. Depending on the task, you might need to collect a lot of the data, or review it manually, or pay a lot for compute, or wait a lot for compute. And still have to iterate a lot on the results, and do your own explorations as a human operator/driver of the whole thing. And then create the materials, test them, get funding to do the whole thing... so theoretically, I think we are in a place where we can successfully apply models to a lot of things, but realistically, we won't be applying all the resources to everything
That's the pitch of LLMs lol
Edit: I somehow missed that this is about magnetic semiconductors (not superconductivity) so DFT is a bit on better footing here. I still think it’s a bit challenging predicting magnetic ordering at elevated temperature, but maybe not as difficult as superconductivity
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.
No offense to the person writing this (assuming they did at all), but I'm not sure they really understand what they're doing..
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?
Are you trying to say that human brains are incapable of inference?
Of course I don’t know how it got its ideas for what to try. But heck, I don’t even understand how I get my ideas half the time. But the process, like what code it wrote, simulations it ran etc can be understood by (some) humans just fine!
It maybe could be possible but beyond the reach of current material science.
With how magical handoff / continuity / whatever it’s called is, it is baffling to me why Apple allows the HomePod to so aggressively take over requests when it sucks so, so much at it.
but the fact that it proceeded in the way that it did was absolutely fucking phenomenal
I’m actually really glad that it was brought up as an example because I had forgotten about it and it’s one of the few kind of hopeful things that we’ve done recently.
The story about OpenAI's Navier-Stokes solution is a good example of what I mean. I don't think it would have been possible without computer assistance because that proof is long and complicated. I'm also not sure that it would have been possible without a human proposing a new approach to the problem, because by all accounts that's exactly what led to the absurd amount of spending that OpenAI did to solve the issue.
I feel like that at least implies that there's some room left for humans in the new world.
More generally, anything can be said about anything.
In magnetic materials, you must calculate separately the current with spin up and spin down and there ara meny interesting applications. My favorite is[1] https://en.wikipedia.org/wiki/Giant_magnetoresistance
[1] Was. Because it has used for hard disks (see the applications section). Now SSD ruins the interesting anecdote.
We live in interesting times.
Linguistic questions were one of the first knowledge categories I trusted LLMs to be able to answer well - quite literally being models of language. It would be pretty shocking for a ~frontier model to get something like that wrong in the last like 3 years at least.
It's a small machine, so it might get bogged down