AI-generated magnetic materials, physics-validated
150,000 AI-designed MOF candidates
10x the coverage of the existing QMOF dataset
Built on a decade-old, 40,000-crystal materials database and validated with DFT physics — now powering autonomous, AI-driven discovery for organic materials.
Who We Are
A decade-old materials database, now an AI discovery engine
Doublet Labs grew out of a condensed-matter research group that spent a decade building one of the largest electronic-structure databases of 3D organic crystals in existence — 40K+ crystals with electronic, magnetic, and crystallographic properties.
That database is now the proprietary training corpus behind our AI discovery pipeline, built on peer-reviewed foundations: PLOS ONE 12(2): e0171501 (2017), and Nature Physics (2021) (Geilhufe, Olsthoorn, Balatsky).
Traction
Our AI has generated 150,000 candidate magnetic MOFs, physics-validated with DFT — 10x the coverage of the existing 20,000-entry QMOF dataset, filling in magnetic-moment ranges the field hadn't reached yet.
Mission
Compressing a 20-year discovery timeline
Trillion-dollar industries — EVs, renewable energy, quantum computing — are stuck on yesterday's materials. Discovering a new functional material the traditional way takes 10–20 years. Organic and hybrid materials are the missing axis of AI-driven discovery: today's AI platforms focus almost entirely on inorganic crystals, leaving organic materials — sustainable, abundant, and environmentally friendly — largely unexplored.
Better magnets
For EVs, motors, and renewable energy — without the rare-earth bottleneck.
Superconductors & dielectrics
Organic high-Tc superconductor candidates, plus the dielectrics that pair with them.
Semiconductors
Band-gap-screened organic and metal-organic candidates from our DFT database.
Qubits & sensing
Qubit substrates and sensing applications built on magnetic MOFs.
Methods
A closed-loop autonomous discovery pipeline
Data
Database: 40K+ organic crystals, a proprietary training corpus.
AI + Physics
The RENGNN neural network combined with DFT constraints generates property-targeted candidates.
Synthesis
CRO partners synthesize top candidates and return characterization data today, en route to an in-house autonomous lab that closes this loop end-to-end.
Feedback Loop
Experimental results retrain the model; every cycle, the platform gets smarter.
Our moat: a proprietary database of 40K+ characterized organic crystals, physics-based AI models (the RENGNN network trained and constrained on DFT band structures), and the autonomous discovery pipeline that ties them together — an agentic AI loop that proposes, evaluates, and refines its own candidates.
See our AI capabilities in detail →Team
Uniquely qualified expertise
Alexander Balatsky, PhD
Co-Founder
A decade building the Database. ~400 papers, 2 patents, APS & AAAS Fellow — brings a long-standing vision for organics in quantum technology.
Avinash Pathapati
Co-Founder
PhD student in AI and quantum materials, building RENGNN — the AI model behind predicting material properties.
Roadmap
From a decade of data to an automated pipeline
2017
Database established
The Organic Materials Database launched with ~40k organic compounds and their properties.
2026
AI + physics-based generation
RENGNN and DFT constraints generate and test candidates — 150k+ new MOFs, property-driven design.
2027
Scaling search and synthesis
Expanding the search space and scaling up synthesis of top candidates.
2028
Early commercialization
Client-driven material generation begins.
2030
Full pipeline automation
Finishing automation end-to-end: prediction through synthesis.
2031
Industry-scale platform
An industry-scale automated discovery platform for organic materials.
Partner With Us
Get direct access to our data and models
Need access to our materials database, AI-generated candidates, or want to explore a joint discovery project? Reach out — we reply personally within one business day.