Accelerate 10x Materials R&D

We bring together scientific AI platforms, World-class materials-science researchers, and automated, robotic-controlled labs to accelerate materials discovery by 10x.

Technology

Large Quantitative Models (LQMs)

AI trained on scientific data and physical principles, predicting outcomes in chemistry, materials, and physics.

Autonomous Labs

Self-driving labs operating 24/7 with robotics and AI orchestration.

Large Language Models (LLMs)

AI trained to excel at understanding and generating human language from unstructured text data.

Physics-Chemistry-Quantum Stack

Integrating high-fidelity simulations and hybrid quantum-classical solvers for accuracy and reproducibility.

Closed-Loop Discovery

AI proposes → Labs test → Data refines AI. A continuous compounding data flywheel.

Digest thousands of papers and data sets.

Performs in silico 100s of experiments daily, recommends top 2-3 candidates daily.

World-class scientists guide AI to train and focus on the right problems.

AI Orchestration transforms discovery process into measurable scientific and business value.

Completes iteration, feeds back lab data in 48 hrs.

Solutions

Compresses R&D timelines from years to days.

Hundreds of prospects with annual R&D budgets of $1B -$15B that need to cut costs and accelerate innovation.

DaaS

Discovery-as-a-Service outsourse R&D bottlenecks for validated breakthroughs faster and cheaper.

Joint Development

Co-develop material solutions aligned to performance and cost targets.

Future Extensions

Data-as-a-Service and Production Automation-as-a-Service.

Industries We Serve

Energy Transition

Utilities, renewables, CO₂ capture

Mobility

Automotive, aerospace, marine

Defense & Security

Advanced materials for resilience & propulsion

Chemicals & Manufacturing

Catalysts, membranes, polymers

Impact on R&D (PEM Membrane Example)

Traditional Discovery Cycle: 4 - 6 weeks per membrane

B10 target: 48 hours per membrane

B10 target: 100 - 300 in 12 months

B10 target: AI-Driven

B10 target: $50K - $150K

B10 target: 6-10x faster learning

Focus Areas

Carbon Nanotube Applications

Stronger-than-steel, lighter-than-aluminum materials

Next-Gen Batteries

Solid-state and lithium-sulfur chemistries with 3× energy density

High-Entropy Alloys

Ultra-durable metals for aerospace, defense, and fusion

Electrochemical Carbon Capture

<$100/ton CO₂ removal

Green Fuels

eFuels and SAFs at fossil-equivalent cost

Interested in partnering, investing, or collaborating?