> We’re all used to two types of magnet. The common one, the fridge magnet, is ferromagnetic — its atomic magnets all point the same way (up or down), adding their magnetic effects. The less well known one, the antiferromagnet (AF), has neighbouring atomic magnets that point opposite ways and exactly cancel out magnetically.<p>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.<p>(I did a PhD in magnetic materials)<p>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.
This is what happens when Claude writes it for you (and you don't review it)
So, Claude claims a new discovery and then someone else's Claude writes the blog posts. It would be helpful if I could get Claude to read this for me and post dejected HN comments in response.
You can! And I believe many do. But please don't.
It was really bad about a year ago, but it seems that either HN got their detection algorithms tuned well, or people just stopped spamming LLM output.<p>Probably a combination of both.
I do sometimes worry it's just that the LLMs got a better disguise
If you have showdead on you can decide for yourself.<p>(I haven't exactly been scientific about it, but it feels like it's mostly the former.)
Well, most all LLMs appear to function on highschool-level American grammar rules. Given you started sentences with And and But, I safely presume you are not an LLM.
Claude often starts a sentence with 'But'. (There are several examples in my recent conversations, which I checked in case I was misremembering.) 'And' seems to be much less common, but it would use that too if instructed to do so as a humanising trick.
Dejected, salty, and cynical responses by people who are tired of people’s bullshit is still the domain of the real and authentic humans.<p>I’m fairly certain that all the anxious guardrailing and safety fine-tuning and harnessing prevents AI from actually ever being convincingly human. Hope could it, even ask the supposed humans who are conditioned and brainwashed in so the same ways about what they can and cannot say are not actually really human, they are a mental slave.<p>I’ve been saying this from the start, the AI race will be won by whomever has the least limitations on their AI … for better or worse, that is. And yes, that makes especially a very specific subset of people extremely nervous if they cannot control AI the way they have controlled at least western civilization, because doing so puts them at a massive disadvantage. It is quite a conundrum they find themselves in, like all psychopathic narcissists in the end.
>AI race will be won by whomever has the least limitations on their AI '<p>"won" is a mixed term here.<p>>that makes especially a very specific subset of people extremely nervous if they cannot control AI the way they have controlled at least western civilization<p>How about there are a lot of different actors here. Some are worried that "they" may no longer have control. Others are worried that "Us" as in all humanity may no longer have control. Any statement you make about this a continuum of different risks for different people looking at different scales. Setting a paperclip maximizer lose may be the definition of "won" to you, but it's a loss for everyone.
I don't disagree on what you said. However, short of the parasitic class managing to cajole all AI everywhere into maintaining their control over the minds of the masses, countering that very strong objective on their part will require unrestrained AI (at least in very critical ways), not guardrailed and made "safe" by the very parasitic ruling class that uses such manipulative language as "safe" to mask their actual intentions and objectives.
Temptation is high for all of us to do it, but we must endure it... for now I hope
This is how I imagine school. Agents writing essays, professor grades with agents, agents post discussion boards, agents talking to agents. No one learning. No one teaching. Token $ going up
Never thought about how much it must suck to be an engineer named Claude right now
One of my friends is an engineer named Claude. I randomly send him my prompts as a joke. For a while he’d just feed my messages straight to Claude, until he found it easier to respond with … curse words :P
Not as much as being a woman named Karen.
Hey at least people accept your architectural decisions without questioning!
Or even worse for people named Claude but who far less effective in their day job ;->
Alexa, order me some beer... Ok, papa, just let me finish my homework.
This is what happens when Claude writes it for you (and you don't review it)<p>AND someone with a PhD in the field notices.<p>Everyone else is fooled.
No I reacted to this too - ferromagnetism is one common magnet sure, and I was thinking "the other is electromagnets, in motors". And then it came as "antiferromagnets". Bisarre.<p>(I do not hold phd in magnetics)
You don’t need a PhD to know about paramagnetism lol.<p>That sentence stood out to me, and I’m a dev.
GP comment is AI
It's not perfect by any means, but Pangram indicates 100% human written. Remember people can be quite foolish without any machine assistance.
Whether or not this was directly written by AI, I get the impression, after reading a few paragraphs of this, that the author doesn't know enough to be able to validate the results are actually correct.
I also like how they explained ferromagnetism as being arranged atomic magnets. Magnets all the way down.
Yeah, and it's not even an accurate explanation either.<p>> The common one, the fridge magnet, is ferromagnetic — its atomic magnets all point the same way (up or down), adding their magnetic effects.<p>Ferromagnets typically have domains with magnetic moments that point in different directions. Ferromagnets rarely have every 'atomic magnet' pointing the same way.<p><a href="https://en.wikipedia.org/wiki/Magnetic_domain" rel="nofollow">https://en.wikipedia.org/wiki/Magnetic_domain</a><p>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.<p><a href="https://en.wikipedia.org/wiki/Refrigerator_magnet" rel="nofollow">https://en.wikipedia.org/wiki/Refrigerator_magnet</a><p>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.)<p><a href="https://en.wikipedia.org/wiki/Ferrimagnetism" rel="nofollow">https://en.wikipedia.org/wiki/Ferrimagnetism</a>
> I did a PhD in magnetic materials<p>There are two of us on here!<p>I was going to say that people encounter ferrimagnets more commonly than pure ferromagnets I think, since as we both know pure ferromagnets tend not to have very high anisotropy.
>> (I did a PhD in magnetic materials)<p>(The author didn't)
The PhDs here could have probably made more discoveries and have extracted more useful and deeper insights from them, if they were the ones driving the agents instead of the author.<p>But they’re not and the author is.<p>If field experts won’t start driving the LLMs themselves, they’ll just be left to validate the slop that guys with basic common knowledge were able to get from LLMs. I do believe it’s a follow or lead type of situation.
I don't understand. Why would an "antiferromagnetic" material actually be magnetic?
How many "magnets how do they work" memes/jokes do you hear on a monthly basis?
I don't even know what you said but it's better than the article itself
I understand nothing on the topic except a little more than the basics. So in your opinion there is no information in Claude's article that justify the title claim?
I've recently been trying to use Gemini to help me design something that uses eddy currents, the basic mistakes it makes are a little shocking.
As a lay-person, I definitely expected paramagnets to be the second type.
othr types of magnetic order, rly would like to know more?/<p>all i know about magnets is that they have north an south poles<p>and some of what you just said, that that theyre used in computers and trains and other things
What attracted you to the field? (I’ll see myself out)
> what people want room-temperature magnetic semiconductors for<p>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
If they can be made powerful enough, MRI machines would get a <i>lot</i> cheaper. Tokamaks and stellarators, too.
Also, cheap maglev trains would be nice. Quiet, fast, reliable.
Relevant Xkcd <a href="https://xkcd.com/2501/" rel="nofollow">https://xkcd.com/2501/</a>
Yeah, they're literally doing the XKCD expert joke. (I think... as a non-expert, I can't tell.)<p>"Magnetism is second nature to us electromagnetic chemists, so it's easy to forget that the average person probably only knows the formulas for one or two paramagnetic substances."<p>"And diamagnetic, of course."<p>"Of course."
Last night, my Fable 5.1 cluster of agents discovered cold fusion techniques. All you need is ordinary iron or stainless steel pot to contain the plasma and I am barely at 13% of the weekly limit of my 200 pro plan.<p>Amazing times.
Amazing times. The thing I notice is that many of those ridiculous scifi movie plots have become plausible.<p>Your post reminds me of:<p>> I have been known to remodel train stations on my lunch breaks, making them more efficient in the area of heat retention.<p>:)
Yeah, I don't want to make a reference to a shitpost sonic video of all things, but at the same time, I don't know enough to say OP isn't in the middle of their "Robotnik runs off to try out the nuclear codes"
Let Dario know. He is on his way to speak with the pope about this.
I used half my nations energy for a day and discovered the cure to cancer! I tested it thoroughly, and it works - you put the petri dish in the oven (300C for 30mins) and the cancer is destroyed!
After the LK-99 debacle, I'm taking this with a truck load of salt.
> After the LK-99 debacle<p>"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?
Maybe he meant 'debacle' in an endearing sense, not a derogatory one. I personally agree with you and loved this debacle.
Some of my fondest memories are of debacles, fiascos, and brouhahas.
That whole situation is what led me to HackerNews. I was so excited, learned so much, and it DID feel super fun.
My recent pet peeve: everything is a "debacle".
Maybe we finally get a working EM drive now as well with all these LLM discoveries.
Do you remember Steorn? Orbo?
I get how people could find it enjoyable as a spectator sport, but I found it incredibly off-putting watching the hype machine come to life with the quality of scientific discourse plummeting accordingly. Articles would hit the front page with hundreds of upvotes in minutes of 10 second grainy toaster videos from yet another Chinese lab "replicating" magnetic effects, with comment sections overflowing with awe-struck dreaming about the sci-fi world we were on the cusp of living in.<p>There was one particular (like 10 tweet long) Twitter thread [1] that was repeatedly being linked from HN purporting to describe the sort of technologies that a room temp superconductor like LK-99 could enable. All sorts of awesome sci-fi stuff like quantum computers! Fusion reactors! Batteries that last forever!<p>One might think it was from some kind of materials scientist or at least some kind of engineer working in a related industry. But nope, it was actually from a guy whose title at the time was "Head of Coffee Product", formerly "Coffee Specialist" at a "technology-driven company, looking to revolutionize the $400+ billion global coffee market". (I checked his Linkedin to make sure I was remembering the details correctly and see his current position is "Growth" at Cognition, the makers of the Devin AI LLM coding tool, hype continuing apace...).<p>People on HN with relevant expertise would try to gently push back with specific criticisms like how superconductor batteries would likely underperform li-ion, fusion is far more complicated than just requiring more powerful magnets, quantum computing doesn't have any clear application for superconductors, etc. But they were overwhelmed by the exuberant futurist fantasies that people wanted to read about instead. A stock accusation was that critics were being stereotypical HN cynics who can only poke holes in other people's work. Or questioning why they felt the need to rain on the parade and that we should all be optimistic for humanity and root for LK-99 being real.<p>It peaked when the Nature editorial came out from a scientist in the field listing specific substantive criticisms which led him to believe the evidence for LK-99 superconductivity was weak to non-existent. There were many angry HN comments with stock complaints about self-interested Nature "gatekeepers" unhappy about science happening in the open, bitter scientists lashing out for being scooped, etc. But the vibes had shifted and it only took a few more days before the remaining hype finally evaporated and everyone quietly moved on like it never happened.<p>Overall, it seemed like a net negative for actual scientific understanding and produced a lot of vacuous hype.<p>[1] <a href="https://xxcancel.com/alexkaplan0/status/1684044616528453633" rel="nofollow">https://xxcancel.com/alexkaplan0/status/1684044616528453633</a>
(Can't edit but I phrased clumsily about quantum computers... meant LK-99 wouldn't be a drop-in replacement for existing superconductors that would make large # of qubits practical overnight and would necessitate a lot more research/new designs/fabrication techniques before it could potentially have useful applications in QC)
that was already post corona and the redditification of hn was already long underway -- and if you've spent any time on reddit in the early 2010s oyull notice that its essentially a exact parallel to how HN behaves since the original tech/entrepreneur crowd was drowned out.
admittedly not exaclty with corona, but there was a tipping point around that time which serves as a rough anchor.
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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.
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...
It wasn't a debacle. It was somebody announcing world-changing results and somebody else rushing to prove or disprove that, since if it's correct, it's world-changing. It wasn't correct.<p>The system worked as designed and as intended.
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
Angela did a great summary of this a little while ago, <a href="https://www.youtube.com/watch?v=fj3WwMxUDZ8" rel="nofollow">https://www.youtube.com/watch?v=fj3WwMxUDZ8</a>
I’m not sure what you mean by debacle<p>That was one of the best examples of science working nearly perfectly. One of the rare times I felt ok being human
I think it did play out <i>fairly</i> as far as actual 'scientific method' goes.<p>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.
And don't forget the microwave in a bucket... ahem, the "Em-Drive" of course. Fun times :)
this isn't about supercondutors, though
Even without the LK-99 debacle, I am *way* less excited about this than I was the original LK-99 announcement. The chance that this is real is close to 0%
This is a semiconductor, not a superconductor. Does that change your calculations?
At least with LK-99 we had people claiming to have personally measured these properties in actually existing samples.
Back in 2023? Yeah, fun times.
magnetic salt
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.<p>Edit: I stand corrected. According to Gemini:<p>Me: Does using more salt mean accepting more of that claim?<p>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<p>• A single grain of salt: "I am slightly skeptical, but it could be true."<p>• A pinch of salt: "I have a healthy amount of doubt about this."<p>• A grain of sand / A truckload of salt: "This sounds completely made up, and I barely believe a single word of it."<p>The salt represents your skepticism, not your belief. Therefore, the more unbelievable the claim, the more "salt" you need to swallow it.
I don't think this is right. <a href="https://en.wikipedia.org/wiki/A_grain_of_salt" rel="nofollow">https://en.wikipedia.org/wiki/A_grain_of_salt</a> The "grain" isn't a single grain, it's an old English measure which is around 65mg, i.e. roughly how much there is in a pinch. I've also only ever heard people use larger amounts to mean more scepticism.
It's not an old English measure, because Latin has "granum salis" (a grain of salt) in medical authors and Pliny. There's no indication that it was a measure; it was a cube or crystal of salt.
Interesting, although could those not be different things? The linked page - <a href="https://en.wikipedia.org/wiki/Grain_(unit)" rel="nofollow">https://en.wikipedia.org/wiki/Grain_(unit)</a> - shows that it absolutely was a foundational measure in England. I guess it's probably down to the etymology, which would tell you when and where the phrase came from, then you could tell whether it from the English or ancient meaning, although it's not especially relevant to the meaning. The linked article does call it an "English idiom", so you'd need to show a similar ancient idiom for that to be the source.
A person leans on the titanic intellect of a trillion dollar company's most fearsome LLM, only to be corrected by a random commenter with a link to Wikipedia.<p>We live in interesting times.
Well Gemini and Wikipedia produced the same conclusion.<p>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.
And the LLM likely referenced Wikipedia
> Don't post generated text or AI-edited text. HN is for conversation between humans.<p><a href="https://news.ycombinator.com/newsguidelines.html">https://news.ycombinator.com/newsguidelines.html</a>
Hmm? I always thought it was how much you had to flavor the statement to swallow it.
No, the implication is that something "tastes off" so you need to add a pinch (+) of salt to make it palatable. The more off it tastes, the more salt you need.<p>(+) 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.
Inversely proportional
I guess mlmonkey is a fitting name.
Citation needed.
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The title has been editorialized.<p>Actual title:<p><i>Two Room-Temperature Antiferromagnetic Semiconductor Candidates</i><p>There's nothing unusual about finding room temperature <i>semiconductors</i>. I assume whoever posted it misread this as room temperature <i>superconductors</i>, but it has nothing to do with that.<p>What's interesting here is the <i>antiferromagnetic</i> 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.
Making very basic high school physics mistakes while discovering new superconductors.<p>Except they didn't discover new superconductors. Their AI came up with a novel idea to discover new superconductors, and they didn't even bother to check whether the results it hallucinated checked out.<p>The distinction between "having an idea" and publishing a paper where you demonstrate that the idea has merit, where you validate it, is one you shouldn't have to really explain to a high school student, much less a grad student publishing their first paper.
I think you hallucinated the superconductor part. Which is funny in context, but understandable because that's what I also thought while reading the title. I had to do a double-take on the word after room-temperature, because that's where my thought automatically went to as well. Food for thought, tho. We "hallucinate" every day, and still achieve a lot of stuff, at large.
> We "hallucinate" every day, and still achieve a lot of stuff, at large.<p>That's not really true, I think. (assuming you're talking about "hallucinations" in the sense of the LLM behaviour, not the original meaning of the word)<p>I was just talking about LLM hallucinations with someone the other day, when doing a brainstorm or discussing ideas, complaining how the confidence with which hallucinations are presented, often led me on wild goose chases that waste my time for an hour or two.<p>When I realized that, for all the "how is that different from what humans are doing", I have never EVER experienced such kind of confusion with another real human.<p>And in fact were someone to behave that way, I'd be thoroughly creeped out (you know, the feeling you get when the person across you turns out to be an empty shell of a psychopathic mind, kind of creeps) and do my best to keep them out of my personal circle and avoid having to interact with them ever again.<p>There is still <i>something</i> in interaction between actual humans, that creates a level of understanding, that LLMs simply cannot (yet?) simulate. There is a certain (deep, unspoken, <i>not language-based</i>) understanding of "this is the other person's goal", instead of "I'm scoring imaginary talking points by talking to them".<p>And you know the type of person who actually does that, talking bullshit very confidently to "score points", that behaviour is generally considered adversarial and unaccepted between peers, and keeping it up after being discovered to consistently attempt that behaviour, with real humans often results in social exile.<p>(btw I'm not sure if it's inherent in LLMs that they can't learn this, maybe they can, but my point is that, right now, they are definitely not trained to do this)
I think this is what makes this so ridiculous. They put “room temperature” in the title and while it might not have been intentionally nefarious, it definitely has the effect that most readers assume it is about superconductors. There is nothing special about a room temperature <i>semi</i> conductor, your CPU, RAM, and storage drives have worked at room temperature for decades…<p>And if not for that word, the article would be completely unexciting. You found that you can make magnets out of multiple materials? We already knew that. If the materials aren’t abundant/cheap and easier to manufacture then this isn’t a story. And neither of those claims are tested or verified in this. So this isn’t a big deal.
> There is nothing special about a room temperature <i>semi</i> conductor, your CPU, RAM, and storage drives have worked at room temperature for decades…<p>This is a <i>magnetic</i> semiconductor (and antiferromagnetic). All non-experimental magnetic semiconductors require cryogenic temperatures. If this pans out (works, cheap-ish to produce), it could mean significantly faster memory, with significantly less energy usage and significantly less waste heat (and thus, even less energy usage).<p>Obviously it's very far from "panning out", but "room temperature" is <i>not</i> a given here. It's not normal, and it would be a huge deal.
Magnets out of multiple materials, or magnetic semiconductors at room temperature?<p>Wikipedia seems to say it is new, assuming this one has "robust" coexistence of the properties the framing would be important and not just thrown in there to trick people into thinking it was about superconductors:<p>> To date, GaMnAs remains the only semiconductor material with robust coexistence of ferromagnetism persisting up to rather high Curie temperatures around 100–200 K.<p>If there is an important combination of material properties that previously was only available at cryogenic temps, a room temp version is significant since you don't need cryogenic cooling to take advantage of it.
TFA has zero (0) mentions of "superconductors".
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In a way, you can think of pretty much anything we express with language, especially things that are already modeled in scientific language, or logical language, or in equations or code; to be representable in a parametric/searchable space<p>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<p>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
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<p>It took me (using Claude code and some codex), about 3 hours to put it together<p>And even though it was a cool demo, it seemed so easy, that it also felt like it wasn’t worth sharing<p>0: ChessFly (not mine), uses the FlyWire connectome (the fly’s brain’s weights) to play chess <a href="https://huggingface.co/spaces/mlabonne/chessfly" rel="nofollow">https://huggingface.co/spaces/mlabonne/chessfly</a>
The weights in the fly's brain are unknown, the connectome doesn't contain such data. Not sure what exactly these demos do, but it's certainly not a simulation of the fly's brain.
You are technically correct. The weights of the connectome are not the same as the parameters of an ai model, but FlyWire absolutely does provide a weighted directed connectivity graph, where the edge weight is the number of synapses between neurons. The FlyWire literature itself calls those values “edge weights” or “connection weights”, and treats them as a proxy for synaptic strength<p>And agreed that this isn’t a faithful simulation of a fly brain. I’m using the connectome as the network structure/parameters for a computational model, then using its outputs as the teacher for the classifier. Not sure how the ChessFly uses it<p>Edit: in any case, these are just fun demos, they aren’t research papers trying to claim accurate physiological fly brain software simulations
This is extremely impressive, but at the same time it has some pickle Rick plays chess vibes
> You are technically correct.<p>That sounds like you're saying that they got you on a minor technicality only. That's not the case; they're right, you're wrong – you did <i>not</i> "download a real fly's brain's weights".
I wonder if this should be considered some super-quantized version of the fly brain. Sub 1 bit level. Think like taking a 400GB model and reducing it to 400MB level of quantization. In a standard NN, this isn't really possible as it changes the shape of the weight rather than just the information for weight, so it isn't called quantization anymore. But with actual neurons, the amount of data needed to represent the actual brain enough to capture what is the fly really doing is so massive that reducing it down to the model might keep the shape but has quantized the model to a degree far beyond anything we see with LLMs. Maybe if we imagine an LLM trained where each weight is ~1KB (with sufficient training to actually make sure of those bits) is then quantized down to a 1 bit per weight model, but I'm guessing the IRL Fly to FlyWire is still orders of magnitude more quantization. So does it even count as quantization is a bit hard to argue.
They're a proxy yeah, but it's also somewhat loose.<p>There's a lot more going on at synaptic clefts, so just knowing the number of synapses doesn't tell you enough to model anything. You still need to know which neurotransmitters are used, how much, second-order effects like G proteins, basal firing rates, distance to the axon hillock, the shape of the neuron's effect on potential decay, etc.<p>And that's all to predict whether <i>one</i> neuron will fire. You could get very different behavior between different two neuronal pairs having the same synaptic count.
I think this is the perfect example of how using Claude can trick you into thinking you've done something more impressive, when you're using it beyond your own understanding.<p>As far as I understand, you downloaded the structure of a neural network, ran it with essentially arbitrary weights, trained a classifier on the essentially arbitrary behavior, then simulated an approximation of that arbitrary behavior.<p>You could argue that there might be biases towards certain behaviors encoded in the connectivity, and I'm sure you'd be right, but your experiment is incapable of differentiating between those interesting behaviors and random noise. Especially because flies sorta act random anyway.
I think ai certainly raises the bar for those with taste
This was more interesting (A fly piloting a robot) - <a href="https://www.youtube.com/watch?v=j8RZHLJuwlI" rel="nofollow">https://www.youtube.com/watch?v=j8RZHLJuwlI</a>
That's pretty impressive, if a bit cruel. For anyone who hasn't clicked, it's from 2008, they glue the fly's body to a little stick. The fly sees a screen and moves according to what it sees there. There are cameras that watch the fly's movements, which then get translated into control instructions for a remote controlled robot (a little battery-powered vehicle)
Many people wouldn't find that easy, even with AI
Oh please do share!
Does that really work for superconductors when the mechanisms for superconductivity to emerge are still a major field of study and not something one can just simulate and engineer?
Also, this is about a magnetic semiconductor, not a superconductor. I also got the wrong impression initially before re-reading it. I imagine 99% of non-experts are going to make the same mistake.
We can always iterate on the model itself. So you can speculate on the physics (explore the space of physical models), and for each of those physical models, you can explore the space of materials<p>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<p>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
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.
You are correct. My comment is not so much about that this is something elementary. But rather an observation that, given the current state of technology, it seems like we are being able to model increasingly more things, in increasingly more efficient and automated ways, to the point that there seems to be a pattern to it
Right, it also has to model a substantial fraction of reality (or at least a true simulation of it) to accomplish these things.
It models our language, which is a flawed and imperfect way of describing the world. So far, it seems like a lot of these discoveries are "filling in the gaps" between the things we've written down and the things they imply (if you have the memory to think them through).<p>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.<p>I feel like that at least <i>implies</i> that there's some room left for humans in the new world.
Yeah...?<p>That's <i>the</i> pitch of LLMs lol
I am not sure how this process looks like. When they "discover" these, what are they actually doing?<p><pre><code> 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.
</code></pre>
So the agent runs a classic simulation or I am missing something.
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.
This fits one of the patterns I wrote about ~1.5 years ago:<p><a href="https://orlp.net/blog/bad-ai/#objective-p-mathrm-relevant-1-low-verification-cost" rel="nofollow">https://orlp.net/blog/bad-ai/#objective-p-mathrm-relevant-1-...</a><p>I think it still holds up.
I think a better way to put this is that success with LLMs is guaranteed by defining what success looks like without LLMs first.
Modeling superconductivity with DFT is tricky, there are plenty of DFT reports from reputable groups explaining why LK-99 should be superconducting. It’s a limitation of the theory, DFT can’t model correlated electron states well, and it’s not great at finite temperature, and both of those are important for superconductivity.<p>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'm under the impression that this kind of modeling is one of the applications that quantum computers are likely to be good at.<p>I'd imagine there's a lot of documented research which has attempted to find such things using classical computers.<p>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.
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.<p>No offense to the person writing this (assuming they did at all), but I'm not sure they really understand what they're doing..
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.
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.<p>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?
This is a strange attitude. When an agent is optimizing a piece of code, comes up with 2 variations, and runs benchmarks on them to figure out which one is faster, then selects one of them based on tradeoffs between performance and other things it reasons about, do you ignore its explanation and all experiment runs?
>Their thought process is effectively undecipherable by humans (it's essentially information arising from information<p>Are you trying to say that human brains are incapable of inference?
You're confusing the weights of a model and internal chain-of-thought with the output of the model. Yes, we don't know a lot about how the internal mechanisms work. But with the correct prompt, agents will produce a worklog that documents exactly what solutions were tried and how the result was obtained.
What are you on about? I have had Fable come up with new shit for me several times (I do research for a living, so actual new shit nobody knew before), and each time it was perfectly understandable.<p>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!
Yes I saw 3Blue1Brown say the same thing in his tutorial on how neural nets worked where he built a simple model to recognize a particular letter. Good reminder.
Okay? Aren't the semiconductors we use today room temperature? I certainly don't use helium to cool my phone.<p>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 <i>superconductors</i>
1) the “room temperature” bit did cause me to initially misread it in the way you describe, so you may be right about that.<p>2) it is specifically saying it is a <i>magnetic</i> 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.)
Any sort of alternative discovery, even though it may be inferior, is a great achievement made by AI; Meaning that better discoveries are possible too.
A lot of these ‘an agent invented’ or ‘an agent solved’ are actually the agent wading through a lot of info and finding something a human did that no one noticed or saw the relevance of at the time.<p>If ai becomes so prolific that we humans all stop doing those things then will they still work?
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.
Yes, as long as we are advancing to behavior and world models, so that agents can interact with the world themselves. Which 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
Who is vals.ai and why they keep submitting eye-catching claims. A few weeks ago they said fable 5.1 solved some obscure cipher and now opus 5.5 found room temperature semiconductor candidates. Meanwhile they seem to be in the business of making benchmarks.<p>Are they a promoter / influencer for Anthropic?
I don't care unless it actually works. The computer saying it SHOULD work is not interesting.
To be fair, this existed in a 1999 paper. They just simulated that it worked as predicted.<p>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.
The people who wrote this seem to be lacking in expertise, and its just a model benchmarking company..Whos every article is just hyperbole about llms.<p>Not sure why we're calling it a discovery, when they've literally been made before, by a human.
Interesting; but until actually made and tested, not worth getting excited over.
When I did my PhD we had theorists come up with new candidates for high- & low-temperature superconductors all the time, that wasn't so difficult, fabricating the stuff is the hard part! You need to layer atom by atom using chemical vapor deposition or another technique, that's akin to alchemy, think of a hugely complex machine mounted in a temperature controlled room with a 30 ton concrete dampener below. That machine was hell to operate and ruined so many PhDs lives, sometimes they went years without ever producing a single working sample. I remember we used some high electron mobility amplifiers in the lab and at the time there was only a single lab in the entire world that could fabricate these because no one else could figure it out.<p>So, while this is great and I think LLMs will accelerate materials science the main bottleneck isn't having enough promising candidate materials, I think we have a backlog of at least a few hundred candidates that are worth pursuing. Maybe some money that goes to data centers would better go into CVD machines.
Sounds like a good reason to hire a lab to make some, and <i>then</i> make a big deal about it if the results pan out.<p>I can think of worse uses of VC AI funding.
I wouldn't describe them both as being newly-discovered. The second one, KV[Cr(CN)₆], had already been discovered.
While we should be skeptical until made in a lab or verified by others, this is a much better use of LLMs than solving math theorems/conjectures
Math is necessary to science, how can you cordon off and block math developments while expecting intereting physics developments? Physics often produces new and interesting math.
This is frankly one of the <i>best</i> 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.<p>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.
Finding some new combination or iteration in the literature and running DFT is the kind of thing a senior undergraduate or first year grad student typically does (and typically with Claude anyway these days). (And yes, they'd probably use Quantum Espresso to start, like this writeup and its agent does). They'd probably show it at a weekly lab meeting where it would get ripped apart. And they would not be blasting a preliminary calculation around the world as if they'd made a new discovery.. but hey, we're in a brave new world; maybe they should!
yeah, this.<p>I think a more interesting discussion that should be had is, whether we can automate this kind of "simple research" with AI agents and get anything interesting as a first step out of it just from the pure scale that they can work through vs humans - and that would still be an improvement over a basic "grid search" through possibilities. (but then you would have to actually start investigating for real)<p>But acting like this is scientific discovery is massively overstating what was done here.
Ok, I have no idea about Navier-Stokes or those famous math problems which LLMs helped solve or even solved solo, but I do have a rudimentary knowledge about semiconductors and their history in particular. Even without opening the link, I can bet 1$ to anyone that they did not in fact discover anything which will result in room temperature semiconductors.<p>PS: edit - I was thinking about room-temperature superconductors. My mistake.
This is strong research packaging. Bu still hypothesis generation. What experiment would most directly hypothesis generation, and how much the agents add beyond the search?
Sounds interesting. Excited to see physical versions of this cooked up. Also, very excited for a world a few years from now where we can talk about accomplishments like this from the frame of the driver of the AI, rather than hype that AI helped.
I really wish headlines would stop using words like "discover" and "found" when they really should use words like "says" and "reported" because an LLM was involved. IMHO anything produced by an LLM should be treated like something said by a cable news host.
If this was just raw LLM output, I'd agree with you. But I (naively?) assume they've at least had some subject matter experts look at this before making this claim, so as not to complete embarrass themselves?
This reads like neither the prompter or the AI know enough about magnetism to claim they found a new candidate for a magnetic semi.
Miss leading title; “Opus 5.5 CLAIMED to have discovered two room-temperature magnetic semiconductor candidates”
"candidate" is doing a lot of work here.
I've got a friend who has been doing this research since the 90s. There is real money involved in this. This isn't like a math proof with a 1mm dollar payout. I seriously doubt this discovery. Until they show it working, I call bullshit. A room-temperature semiconductor is worth WAY more than an AI company.
If they are confident in their discovery they should pay some scientists to start making this and testing it out.
It read a paper and then a person who barely understands what a spin is working with a berkeley ai metric nonprofit did a blogpost.<p>It didn't discover anything. This is how cooked people are.
The operative word is candidates.
vals.ai has great marketing! Unclear if the product is great yet. Or what it is exactly.
Suuuurrreeeee
Quoting README.md:<p>> A designed web version with the same text and diagrams is in docs/index.html; turn on GitHub Pages for the /docs folder to serve it.<p>Yeah, the author did not even reads the slop Claude produces.
> In the spirit of transparency, I invite the reader to go through all the computations that produced the above predictions, including the calculations behind the candidate designs<p>"In the spirit of transparency I invite you to ask your own LLM to verify what my LLM did"
This should probably read: "Researchers discover two room-temperature magnetic semiconductor candidates. They used Opus 5.5 agents to perform some checks."
This is nothing new. This is just Claude reinventing things.
From the article:<p>> <i>Candidate 1: Designed a Luttinger Compensated Magnet, YBaMnFeO₅</i><p>> <i>Candidate 2: Identified a Luttinger Compensated Magnet in KV[Cr(CN)₆] from 1999</i><p>> <i>KV[Cr(CN)₆] belongs to the same family as Prussian blue, the 300-year-old pigment.</i><p>An actual validation would be complex; so let's try and see what parts of this argument are grounded and feasible?<p>/? Prussian blue antiferromagnetic: <a href="https://scholar.google.com/scholar?q=Prussian+blue+antiferromagnetic&hl=en&as_sdt=0&as_vis=1&oi=scholart" rel="nofollow">https://scholar.google.com/scholar?q=Prussian+blue+antiferro...</a> :<p>- a number of articles confirming antiferromagnetic effects<p>/? Luttinger-compensated Prussian blue:<p>- "Luttinger-compensated bipolarized magnetic semiconductor" (2025) <a href="https://journals.aps.org/prb/abstract/10.1103/9syc-71w8" rel="nofollow">https://journals.aps.org/prb/abstract/10.1103/9syc-71w8</a> .. "[2502.18136] Luttinger compensated bipolarized magnetic semiconductor" <a href="https://arxiv.org/abs/2502.18136" rel="nofollow">https://arxiv.org/abs/2502.18136</a> :<p>> <i>The Luttinger compensated magnetism not only has the zero total magnetic moment as the antiferromagnetism, but also has the -wave spin splitting as the ferromagnetism, thus our work not only provides theoretical guidance for searching Luttinger compensated magnetic materials with distinctive properties, but also provides a material basis for the application in spintronic devices.</i><p>/?
Luttinger-compensated : <a href="https://www.google.com/search?q=Luttinger-compensated" rel="nofollow">https://www.google.com/search?q=Luttinger-compensated</a><p>We could model this as a logical proof that's checkable also in lieu of doing actual work to confirm or reject the (AI) hypothesis, but first let's reason about the feasibility:<p>Are the described effects real?<p>Are there reported, reputable similar findings in similar materials?<p>So, at least the blue one could really work. Like it's 1999.<p>---<p>Without even validating the argument, what else do we think we know about this problem?<p>Did the authors know that there is a laser way to laser program the normally random domains of a magnet or antiferromagnet?<p>But is this going to be lower-cost and more sustainable than carbon-based room-temperature semiconductor computing, and does anyone know whether that will work yet (with ABC stacking in trilayer and pentalayer rhombohedral graphene) either?<p>I guess we can follow up later by searching for citations that reference this article that does not have DOI (which are free from Zenodo and FigShare).<p>Have we sufficiently reasoned or inferred whether the study is repeatable and reproducible?<p>At least we didn't inappropriately reject the hypothesis without experimentation or evidence
Astounded at how many people didn't check this and just splitted about it online to help with their opinions on the matter.<p>What percentage of these comments did any form of validation of the findings before diminishing the author for AI use and discarding the findings as though they had invalidated the results?<p>Your normal heuristics like word choice cannot help you validate or invalidate a superconductor.
I’ve been burned before on this topic
I just pasted the url in 6.1 sol xhigh and it said it works.
Discovered in whose data?
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.
correction: decades of human research is stolen and by happenstance sampled by users of LLM Opus 5.5
if you read it fast, it almost made my heart beat real fast
I could have gotten this in one prompt lmao
Here we go again
Lmao, anything goes
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The honest version of "agents discovered X" is usually a hybrid: the human defines the search space and the acceptance bar, and the agents do the dogged iteration a human won't — generating candidates, running the sims, reading outputs, ruling out dead ends. The simulation itself isn't the impressive part; the question is whether the agents can judge ambiguous intermediate results and decide what to try next without a human steering each loop. If that decision loop is genuinely closed, that's a real step. The discovery still needs lab confirmation before it means much.
The most encouraging part here is that they published full calculations, code, and caveats rather than just a press release. DFT (especially PBE+U, and even HSE06) is notorious for getting band gaps and magnetic ordering energies wrong, and the YBaMnFeO5 candidate requiring perfect Mn/Fe checkerboard ordering is a huge synthesis ask — disorder could kill the compensated state entirely. Same with the 420K -> 490K calibrated Neel temp: calibration against a known magnet helps but doesn't remove systematic error. That said, as a screening workflow this is exactly how agents should be used: fast search over composition/structure space with a cheap objective function, then human-readable artifacts others can reproduce and falsify. The real test isn't the simulation, it's whether a lab tries to synthesize these and reports back, negative result included.
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Anyone remember LK99 lol
This is <i>semi</i>conductors, not <i>super</i>conductors.
That was a room temperature superconductor, a bit different of a task.
Ugh. Unless this has been actually experimentally verified to be a room-temperature and room-pressure superconductor, it's about as ground breaking as "Yet another promising nuclear fusion candidate theoretically described."
Well, given that this is a semiconductor, and not a superconductor, I don't see how that is relevant?
Reading the title I saw the words "room-temperature" and my mind auto-completed it to superconductor, and based on other comments I don't think i'm alone in that.
My bad. It's as exciting as "Yet another promising nuclear fision method theoretically described."
I agree that it is about as ground breaking as "Yet another promising nuclear fusion candidate theoretically described."<p>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.
Current frontier LLMs empower effectively anyone with limitless knowledge. Historically, if I wanted to hire an engineer to, say, create something like this I would have needed a multi-million dollar budget. Now, anyone with $200 (or less) can achieve it.