MatterSpace
Material discovery use cases where invalid work is the real bottleneck.
MatterSpace supports material and energy programs that need candidate sets worth simulating, ranking, and synthesizing under real physical constraints.
Built for material and energy programs where the cost of invalid candidates dominates the workflow.
Example Programs
Where MatterSpace removes invalid work.
Real programs where constraint-aware generation changes the shape of the workflow.
Blind Rediscovery of SAA Catalysts
Public rediscovery report available
MatterSpace demonstrated blind rediscovery on single-atom alloy catalysts, recovering sealed targets to sub-angstrom accuracy without any target information in the generation process.
Battery Cathode Discovery
Balance energy density, lifetime, and safety
Use MatterSpace when cathode programs need new candidate material that respects stability and feasibility constraints instead of collapsing late in evaluation.
Solid-State Electrolyte Search
Target conductivity and compatibility together
Generate electrolyte candidates against conductivity, structural, and cell-compatibility requirements so the shortlist is closer to the real battery architecture.
Thermal and Protective Coatings
Durability under operating conditions
Explore coating material and surface-system candidates when the job is to balance adhesion, environment, and durability without wasting cycles on poor fits.
Magnetic Material Programs
Search with performance and supply constraints
Generate magnetic-material candidates when performance targets, temperature windows, and material constraints all need to stay visible from the first step.
Polymer and Framework Exploration
Move beyond narrow family search
MatterSpace helps teams explore polymer, MOF, and metamaterial candidates when existing family-based screening leaves too much of the usable search space untouched.
How It Works
From material requirements to candidate shortlist.
MatterSpace is designed for programs where the goal is not just more candidates, but better candidates that survive contact with the real constraints.
Constraint-aware generation
The product keeps feasibility rules inside the campaign so teams do not discover the basic invalidity of a candidate too late.
Campaigns matched to the program
Open discovery, refinement, guided rediscovery, and blind benchmark modes cover the main material discovery situations without changing products.
Trade-offs instead of single answers
MatterSpace is built to return shortlists across competing objectives so teams can evaluate the frontier instead of accepting one brittle output.
Supported material programs
Batteries, catalysts, coatings, electrolytes, superconductors, magnets, and related material and energy workflows.
Supported Programs
Material and energy workflows.
MatterSpace covers material and energy programs where viable candidate generation under real constraints is the central problem.
Battery and storage material
Catalysis and chemical processing
Superconductors and quantum material
Photovoltaics and solar energy
Thermoelectrics and waste heat recovery
High-entropy alloys
Magnets and magnetic material
Coatings and surface engineering
Solid-state electrolytes
Metamaterials, MOFs, and polymer design
Constraints in. Material candidates out.
If your material program is spending too much of its time rejecting weak candidates, we can scope the right MatterSpace evaluation path.