NASDAQ: RXRX
Recursion Pharmaceuticals
AI-native drug discovery
Public 2021. Peak market cap above $5B at IPO; acquired Exscientia in 2024 to consolidate the AI-bio platform thesis.
Investors·$10M seed·SAFE
Materials intelligence for the next superconductors.
Superconductors that work at higher temperatures would change what power grids, MRI machines, and quantum computers cost to build and run. SuperMatics finds them: AI guided by the MEL physics framework, measured in collaborating labs at UIUC, UC Berkeley, and Georgia Tech.
18yrs
Physics theory
10+
Patents
3
Research groups
±1 K
Back-test accuracy
Timing
Why now?
Generative models now sample thousands of candidate compositions in the time a single DFT calculation took a decade ago. The theory those models need was the missing piece, and it stopped being missing: MEL turned correlated-electron chemistry into a search space a model can learn, and in 2026 a Stanford / SLAC group published the matching experimental signature in Physical Review Letters.
The labs are ready too. Collaborations at UIUC, UC Berkeley, and Georgia Tech already run the measurement cycles that turn a prediction into a confirmed material. Any one of these alone was gating. Together they open the search.
The honest risk is generalization to entirely new material families. That prospective test is what this round funds.
Market & analogs
First revenue lives in a $25B market.
We enter through a concrete HTS beachhead and compound outward. The reference class for a physics-grounded discovery platform is already public, set by pharma-AI.
Near-term beachhead
$25B+
The HTS component and materials-discovery market we enter first, 2024 → 2035, growing ~12% a year, pulled by quantum hardware, power infrastructure, and medical imaging. This is where the first revenue comes from.
Long-term ceiling
$10T+ / yr
The infrastructure that beachhead scales into: the world's energy, compute, transport, and medical systems. This is the ceiling. The first product is a wire, not a grid.
NASDAQ: SDGR
Schrödinger
Physics-grounded simulation for drug design
Public 2020. Has carried a multi-billion-dollar market cap on a physics-first computational thesis. The closest public analog for MEL's positioning.
Acquired
Exscientia
AI-designed molecules
Acquired by Recursion in 2024 for ~$680M, confirming strategic M&A demand for physics- and AI-grounded discovery platforms.
How this returns capital
IP licensingMEL-generated candidates and synthesis routes are patented and licensed to industrial manufacturers, quantum-hardware companies, and energy majors.Year 1–3 · recurring revenue
Strategic acquisitionA physics-grounded materials platform is a strategic asset for semiconductor, quantum, and energy companies with large materials R&D mandates.Year 4–7 · M&A exit
Platform at scaleThe model is established in pharma-AI: a continuously learning discovery platform commands platform multiples once the candidate library reaches scale.Year 7–10 · IPO path
Public market data · cited as reference, not a forecast
Defensibility
Hard to copy.
Physics IP
A head start measured in decades
MEL is patent-protected (10+ filings, counsel by Wilson Sonsini), published on arXiv, and back-tested to ±1 K. A search space built specifically for the high-Tc regime. The theory began in 2006; anyone replicating from scratch starts there.
Synthesis-first
Predictions are cheap. Real samples aren't.
Anyone with compute can generate candidates; the papers already exist. We rank and route only the ones a lab can synthesize. The moat is the few that become real.
Partner network
Techniques, not datasets
Validation lives in labs deeply experienced in each measurement, not on the open internet that AI labs scrape. A frontier model can read every paper and still miss the framework that makes the search work.
Depth
Originator-led science
AI labs can hire ML engineers. They can't replicate the correlated-electron theory behind MEL by reading papers.
From the scientific founder
“The cuprates were found by accident in 1986. Forty years on, we still find superconductors mostly the same way. MEL exists so the next one doesn’t have to be an accident.”
A founding team of three: MEL’s originator, a Rigetti quantum systems engineer, and twenty years of US–Korea operating experienceMeet the team
Recent traction
The last three quarters.
Dated, verifiable. The momentum that earns the next twelve months of capital.
- 2026 Q2External validationStanford / SLAC publish the cooperative CDW-SC measurement MEL predicts (Lee et al., Physical Review Letters, DOI 10.1103/g41t-8456). Read ↗
- 2026 Q2Investor signalPitched at Plug and Play in Silicon Valley. Interest from both their investment and semiconductor teams.
- 2026 Q2IndustrialCAN Superconductors begins build of MEL-generated high-Tc candidates and advises on synthesis routines for scale-up.
- 2026 Q1PaperThe metallic-superconductivity criterion goes up on arXiv (2601.14500). Gold, silver, and copper come out negative, as they should. Read ↗
- 2026 Q1NetworkMadhavan group (UIUC), Georgia Tech, and QB3 / Berkeley Nanofabrication Center engaged for measurement and synthesis.
- 2025 Q4Paper + platformThe MEL framework paper on arXiv (2512.03368), back-tested to within ±1 K. MEL classifier v0.4 in production, wired end to end. Read ↗
Covered in the Korean press since Aug 2025 · the coverage →
The next twelve months
- Q2
Pipeline at scale
MEL classifier and generative model running across focused superconducting and thermal search spaces.
- Q3
Validation cohort
First experimental back-tests on generated candidates. Collaborating groups at UIUC confirm predictions, with additional measurement cycles opening across partner labs.
- Q4
IP wave
Patent filings around generative candidates and process routes. Investor-grade demo of the workspace.
- Q1
Commercial wedge
First partner-led commercial conversations on thermal and superconducting materials.
12-month directional roadmap · specific commitments shared in diligence
Commercial wedges
Focused markets first.
Lead wedge
Higher-Tc superconductors
Quantum hardware · power transmission · medical imaging · accelerator magnets. Industrial scale-up with CAN Superconductors.
Adjacent
Datacenter thermal materials
Heat-spreaders and phase-change media for AI training clusters and high-density compute.
Adjacent
Energy conversion
Solid-state cooling, thermoelectric recovery, refrigerant-free climate systems.
Adjacent
Battery and ionic materials
Solid-state electrolytes and cathodes tuned for high-rate cycling and density.
Use of funds
Where the round goes.
Platform & ML engineering
~40%Founding CEO and Solutions Architect, generative model training, scientific software, and infrastructure for continuous discovery runs.
Experimental validation
~30%Collaborating lab cycles, synthesis access, candidate measurement, and extending the back-test to additional correlated-electron families.
IP prosecution
~15%Patent filings on generative candidates, process routes, and platform components, with Wilson Sonsini as patent counsel.
Commercial & partnerships
~15%Founder-led conversations into semiconductor, energy, and quantum-hardware materials buyers.
Allocation directional · specific budget shared in diligence
Most asked
Asked in every first meeting.
What's the real technical risk?
Generalization. MEL back-tests to ±1 K on the six published superconductors shown on the science page, and an outside group measured its core mechanism in 2026. None of that guarantees the framework transfers to entirely new material families. That prospective test is what this round funds, and the measurement cycles at UIUC, UC Berkeley, and Georgia Tech are how we find out quickly.
When does revenue start?
IP licensing inside Year 1–2 as the first generative candidates hit patent. Discovery partnerships with industrial manufacturers (semiconductor, energy, and quantum-hardware companies with active materials R&D) follow in Year 2–3.
What does governance look like?
Founder-led to date, with Wilson Sonsini Goodrich & Rosati as corporate and patent counsel. As a SAFE round we are not setting board composition at close; governance is shaped in the priced round. Information rights and reporting cadence are agreed in the SAFE side letter for any lead participant who wants them.
Diligence
Terms live in the conversation, not on the site.
Thesis, proof, and milestones live here. The data room, the model, and the term sheet come out once we’re talking. We reply within two working days.