Superconductors that work at higher temperatures would rewrite global energy, quantum computing, and medical infrastructure. They are hiding in chemistry today’s AI can’t search. SuperMatics finds them: generative AI built on 18 years of physics theory, recently confirmed by Stanford and SLAC in Physical Review Letters, and running today with collaborators at UIUC and UC Berkeley.
PRLNew · 2026·MEL mechanism confirmed by Stanford and SLAC in Physical Review LettersBuilt with
Higher-temperature superconductors and the quantum materials around them are gating ingredients in three of the largest hard-tech buildouts of the next decade. Find them and entire industries move.
$300B+
Lossless power, at grid scale
Five to ten percent of every electron sent through transmission lines is wasted as heat. Higher-temperature superconductors reclaim that loss. They also unlock magnetic fusion, lossless storage, and the AI-era power grid.
10,000×
The bottleneck on practical quantum
Every leading quantum computer runs on superconducting circuits. Materials that hold quantum coherence longer are the gating constraint for general-purpose quantum hardware, and for the low-power dense processors AI buildouts demand.
Next-gen
MRI, accelerators, fusion magnets
From clinical imaging to fusion confinement to next-generation accelerators, higher-Tc materials are the gating ingredient. Today every one of these systems is held back by extreme cryogenic constraint.
These aren’t speculative markets. They are the global power grid, the AI buildout, and modern medicine. The materials are the limiter.
Three things we do that nothing else does. Together, they are why leading labs in modern condensed matter measurement have agreed to collaborate with us.
Most AI for materials is trained on simulations that silently fail on the materials breakthroughs actually live in. We start from a physics framework purpose-built for those exact systems, and search inside it.
Generic ML can't see what isn't in its training set.
MEL is the only physics framework built specifically for high-temperature superconductors. Its originator developed it over eighteen years before we wrote a line of platform code. We didn't invent the science. We made it searchable.
Time you can't compress with capital.
Every prediction is measured in collaboration with research groups at UIUC and UC Berkeley, labs deeply experienced in the techniques each prediction depends on. Confirmations feed back as priors. Predictions sharpen with every cycle.
AI predictions only matter when reality validates them.
The technical comparison, DFT vs MEL, lives on /science
Read the scienceA 2026 paper in Physical Review Letters from Stanford and SLAC reports the experimental signature MEL has predicted for years: charge order and superconductivity that phase-lock and grow together, not against each other. The science we sort on is no longer hypothesis.
Old view (competition): charge order suppresses Δ_SC below Tc. Confirmed view: both Δ_SC and ρ_MEL rise together with locked phases — exactly the cooperative regime MEL describes.
Why this is load-bearing
MEL's coupled Ginzburg-Landau functional carries an explicit cooperative coupling γ₁ ρMEL |ψ|². When γ₁ > 0, the modulation locks energetically to the condensate; the wavevector pins to the lattice; Tc gets a lift. The SuperMatics classifier scores candidates on exactly this normal-state stiffness α(q).
Stanford and SLAC tracked the charge-density-wave order through the superconducting transition, with resolution high enough to read its phase coherence directly through Tc.
Below Tc, CDW phase coherence grows in lockstep with the superconducting condensate, and the CDW wavevector locks onto the lattice rather than drifting. The two orders reinforce each other; they do not compete.
MEL's coupled Ginzburg-Landau functional carries an explicit cooperative term γ ρ_MEL |ψ|² and predicts precisely this lock-in. The platform's classifier sorts candidates on this criterion.
From the scientific founder
“We have argued for years that charge order and superconductivity in cuprates reinforce one another rather than compete. The Stanford and SLAC measurement reads the same cooperative coupling out of the data, in the same family of materials, with the same wavevector lock-in. The microscopic story we built the platform on is the story the experiment tells.”
What MEL predicted theoretically, an independent group measured. The platform sorts candidates on the exact criterion this result isolates.
See the formalismForty years of progress. Two physics families. Most of the landscape above the liquid-nitrogen line has barely been searched. MEL searches it, including the unconventional and high-Tc regimes where conventional AI cannot.
MEL hunt range · above LN₂ · novel and unconventional families
YBCO·92 K
The first cuprate to break liquid nitrogen. Workhorse of every commercial HTS magnet today.
BSCCO·110 K
Long-wire HTS used in fault-current limiters and motors. Higher Tc, harder fabrication.
Hg-1223·138 K
Ambient-pressure cuprate record. The highest Tc humanity has confirmed without extreme pressure.
LaH₁₀·250 K
The all-time record, but only stable inside a diamond-anvil cell. Not deployable.
RT·293 K
The prize. An ambient-pressure room-temperature superconductor rewrites power, computing, and medical imaging at once.
Public physics values · BCS conventional ceiling · cuprate family · ambient-pressure and high-pressure records
Five stages, running continuously, end to end. Click any one to step through. The loop never stops. Each closed cycle sharpens the predictions in the next.
Stage 01
Read the material's playbook
Feed the platform a candidate material. It reads the atomic structure and identifies which quantum behaviors the structure can host: superconductivity, charge order, spin order, orbital order. Think of it the way a chess engine reads a board. The moves a position permits are now known.
A live trace of the discovery pipeline, end to end: classifying candidates, generating new ones, screening, synthesizing, closing the loop with a collaborating lab. Hover any line to decode the physics behind it.
events
1,242
confirmations
47
p50 latency
1.8 s
01
Operating temperature
>120 K, ambient
Quantum computing
02
Thermal conductivity
>1500 W/m·K
AI training and inference clusters
03
Efficiency figure
>3 at 300 K
Cryogenic cooling
04
Ionic conductivity
>10⁻² S/cm
Solid-state batteries
Every prediction is measured. Our collaborators run the techniques MEL was designed to be confirmed by: scanning tunneling microscopy at UIUC, synthesis at UC Berkeley, with further research discussions underway across leading correlated-electron groups.
Prof. Vidya Madhavan
University of Illinois Urbana-Champaign
University of Illinois Urbana-Champaign
STM / STS · FT-STS
Experimental collaboration · STM/STS, FT-STS
QB3 / Berkeley Nanofabrication Center
UC Berkeley · Waqas Khalid
UC Berkeley · Waqas Khalid
Synthesis · Fabrication
Research infrastructure · synthesis and fabrication
Czech Republic · HTS manufacturing
Czech Republic · HTS manufacturing
HTS synthesis · Scale-up
Industrial HTS synthesis of MEL candidates · advisory on synthetic routines and scale-up
Wilson Sonsini Goodrich & Rosati
WSGR
WSGR
Patent · Corporate
Patent strategy · IP and corporate counsel
What changed this quarter. Concrete artifacts only: a published paper, signed collaborations, a shipped build of the classifier, and the roles we’re hiring for.
Stanford and SLAC publish in Physical Review Letters (Lee et al., 2026). Resonant soft x-ray scattering on a cuprate shows CDW phase coherence growing BCS-like below Tc with near-perfect wavevector locking. The cooperative CDW-SC behavior MEL was built to predict, measured directly.
CAN Superconductors begins build of MEL-generated high-Tc candidates and advisory on synthesis routines.
Eighteen years of foundational research distilled into one criterion. Back-test agrees with measured Tc within ±1 K across the public superconductor literature. arXiv:2512.03368.
Madhavan group (UIUC) · QB3 / Berkeley Nanofabrication Center for synthesis · further discussions underway with leading correlated-electron groups.
Classification, generative proposal, computational triage, and synthesis routing wired without manual handoffs. Generating candidate batches daily across the platform.
Berkeley-anchored, remote possible. Looking for builders with deep-tech instincts and the patience for hard physics.
01 · Investors
Deep-tech capital at the AI × physics interface. Brief by invitation.
Open investor brief02 · Research collaborators
Condensed matter theorists and experimentalists. If your work is at the frontier of unconventional superconductors or quantum materials, let’s talk.
research@supermatics.io03 · Builders
CEO and Solutions Architect open in Berkeley. Foundational work, ownership stakes.
See open rolesOr write to contact@supermatics.io. We read everything.