Vals AI's agents proposed two room-temperature magnetic semiconductors. Nobody has made either.

On October 4, the AI evaluation company Vals AI published a research note describing two candidate materials for next-generation computer memory, found by a team of more than 90 Claude Opus 5.5 agents working for three days under a human author, Geby Jaff. Both are antiferromagnets that the company's quantum-mechanical simulations predict will sort electrons by spin at room temperature. The phrase that travelled — "room-temperature magnetic semiconductors" — is accurate. The word missing from it is the one that matters: simulated. No sample of either material was made or measured in this work.

What the agents actually ran

Strip away the framing and the work is a computational search. The agents ran density functional theory, the standard quantum-mechanical method for predicting how electrons behave in a crystal, at two levels of accuracy: a fast approximation called PBE+U and a slower, usually more reliable one called HSE06. The band gaps and spin windows in the company's own table come from the more accurate method. The simulations ran on cloud CPUs, and Vals AI says hundreds of them went into the final two candidates. The inputs, raw outputs and analysis scripts sit in a public GitHub ledger, alongside a claim-by-claim checker. That repository is the most useful artefact here, because it lets an outsider re-derive every number rather than take the blog post on faith. Its history also shows the process was not one clean run: one commit records an outside review that forced corrections, including a revised formation-energy figure for the first candidate.

Two candidates, one designed and one rediscovered

YBaMnFeO₅ is a new compound of five elements (yttrium, barium, manganese, iron and oxygen) that the agents designed. Vals AI says it could not find a record of the material ever being made or proposed for this purpose, and its own simulations hint at why. The useful form needs manganese and iron atoms arranged in a perfect checkerboard, and the checkerboard was predicted to fall apart into a random mix at around 950 kelvin, well below the 900 to 1,300 degrees Celsius at which such oxides are usually cooked. The design may be hard to make in the ordered form that gives it the property.

The second candidate is the more interesting story. KV[Cr(CN)₆] was first synthesised in 1999, and its predicted ability to sort electrons by spin appears to have gone unnoticed for about 27 years. Vals AI's phrasing is that the property had been "hiding in plain sight."

Candidate	Predicted band gap	Spin windows	Experimental status
YBaMnFeO₅	2.35 eV	1.0 eV holes, 1.4 eV electrons	Never made; key ordering may scramble during synthesis
KV[Cr(CN)₆]	about 2.1 eV	2.6 eV holes, 1.6 eV electrons	Made 1999; ordered to 376 K; windows unmeasured

Both belong to a category the company calls Luttinger-compensated magnets: antiferromagnets whose up and down atoms sit in different environments, so their spins can still be separated by energy. That separation is the property the storage industry wants.

Why spin sorting is the whole point

An ordinary ferromagnet, the fridge-magnet kind, has a net magnetic field and sorts electrons by spin naturally, which is why it works inside a hard-drive read head. But that stray field interferes with nearby components, and switching a ferromagnet is slow. An ordinary antiferromagnet solves the field problem and switches roughly a thousand times faster, but its spins are not sorted by energy, so spintronic devices have nothing to read. The prize, then, is a material with zero net field that still sorts spins, expressed through the "spin window": the slice of energy at the edge of the band gap where every available state carries the same spin. The benchmark is room-temperature thermal energy, about 26 meV. The windows predicted here are one to two orders of magnitude wider. The blog also notes that a 2025 study predicted two other Luttinger-compensated semiconductors, both of which lost their magnetic order below room temperature, and named a room-temperature example as the open goal.

What this is not

This is the section the headline skips. These are density functional theory predictions for ideal crystals at zero temperature. Nobody has measured the band gap, the spin window or the conductivity of either material. The 1999 sample of KV[Cr(CN)₆] was a hydrated powder that showed a small leftover magnetic moment, 0.125 Bohr magnetons per formula unit, where a perfect dry crystal is predicted to have exactly zero, and Vals AI's two simulation methods disagree about how much the water in its pores weakens the effect. HSE06 says the spin sorting survives; the faster PBE+U method says the hole window shrinks by more than half. The ledger's own caveats state plainly that even HSE06 "can misplace energy levels by tenths of an eV," and that the zero-moment result is spin-only, ignoring the orbital contributions that could leave a small residue. The company itself rates YBaMnFeO₅ as a candidate that "may be hard to make."

How the internet read it

The story travelled. Vals AI's announcement on X drew more than 7,200 likes, and a thread on r/singularity titled "AI Is About to Transform Materials Science" drew roughly 1,800 points and more than 200 comments. Some of that attention ran on a misunderstanding. "I definitely thought they were talking about superconductors at first but it's talking about semiconductors," one commenter wrote; another put it as "This says SEMIconductor, not superconductor." The distinction is the difference between a material for memory chips and the decades-old dream of lossless power lines. The scepticism was blunter than the hype: "Real material, predicted ability. Come back when it's proven," read one reply. The most-liked reply on the X post itself made a similar point in one line, with more than 750 likes: "in 3 days a computer used 40 years of research to calculate a pattern from the 40 years of research."

What would actually settle it

Re-make KV[Cr(CN)₆] and measure it. That is the company's own next step, and the cheapest test available, because the compound already exists and the prediction is specific and falsifiable: roughly one spin channel over about two electronvolts near the top of the valence band, reversing with the magnetic order. Element-specific X-ray measurements and spin-resolved photoemission would probe it directly. The shape of the exercise echoes an MIT group's graphene-agent work last week, where an agent wrote its own simulator and ran it for days: candidate after candidate, but the loop still stops at the model's own assumptions. Anthropic's enzyme finding last month at least reached the bench before the headlines did. For KV[Cr(CN)₆], the bench result is 27 years overdue.

The candidate, not the breakthrough

What Vals AI has is a shortlist and a reproducible trail, not a material. The genuinely interesting claim is the second one: a compound sitting in the literature since 1999 that no one apparently checked for a property it may well have had. Whether that is discovery or a very fast literature review is a fair argument, and the company's own ledger invites you to make it. The prediction is on the table, the code is public, and the test is cheap. That combination, more than the agent count, is what makes this worth watching.