REASON
Select the next experiment using physics, data, and objectives.
Discovery 2 Scale
Materials-to-manufacturing platform built to deliver real, feasible solutions faster, reducing development and certification time while lowering failures associated with commercialization of advanced materials.
D2S connects physics-informed design, robotic synthesis, mechanical testing, qualification/certification, and scale-up validation in one closed-loop materials platform.
Agent mesh
Plan -> Command -> Learn
Robot fleet
Autonomous workcells online
Promising materials often fail after discovery.
Materials can perform well in simulations but still fail during manufacturing, qualification, or scale-up. D2S addresses these risks from the beginning by integrating discovery, experimentation, qualification, and manufacturing validation into one platform.
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Faster Discovery
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Discovery workcell
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New alloys made everyday
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Samples’ mechanical properties are tested every day
Materials Discovery
Physics-informed AI identifies promising candidates based on targets, experimental evidence, and manufacturing constraints.
Synthesis & Testing
Robotic workcells synthesize, process, characterize, and mechanically test materials while capturing structured data.
Qualification
Traceable testing evidence supports performance validation, certification, and customer adoption.
Scaling
Early assessment of processing and production risks helps materials transition successfully from laboratory to manufacturing.
AI + Agents + Robots
A self-driving laboratory powered by AI agents.
COMMAND
Send executable instructions to autonomous workcells.
OBSERVE
Capture processing, structure, and performance evidence.
ADAPT
Use every result to improve the next decision.
Why work with us?
Bring a materials challenge to D2S.
Work with us to discover, test, qualify, and scale advanced materials with lower technical and manufacturing risk. Every recommendation is evaluated not only for predicted performance, but also for processing, manufacturability, and practical pathways to scale.
Autonomy where it matters
Materials scientists teaching AI what matters.
AI is most powerful when guided by domain expertise. Materials scientists define the physics, objectives, and manufacturing constraints. AI agents design experiments, robotic systems generate evidence, and every result strengthens the knowledge base for future decisions.