The Universal Generation Engine for Science and Engineering
A generation platform for valid candidates under real constraints.
Define target properties and hard constraints. Generate candidate materials, molecules, algorithms, or chip layouts that satisfy those rules from the first step.
Every output is valid by construction and ready for evaluation.
Core Capabilities
Spend your time on candidates worth keeping.
Constraints stay inside the search process. Every candidate your team reviews has already passed the rules that matter.
Higher viable-candidate yield
When feasibility checks move inside generation, teams spend their time ranking candidates that already meet the bar.
Constraints inside generation
Stability, manufacturability, and chemical validity shape the search at every step.
Broad trade-off coverage
Design teams receive a set of viable options across competing objectives — a Pareto frontier of real trade-offs, not a single score.
Built-in reproducibility
Every campaign leaves behind replayable evidence so teams can compare runs, revisit assumptions, and hand work across groups.
MatterSpace Lines
Five lines, one universal engine.
Five product lines share the same core engine. Lattice covers materials and energy. Vital covers longevity, epigenetic reprogramming, and delivery design. Origin covers protein generation. Algo and Tessera are in development for algorithm search and chip design.
MatterSpace Lattice
Materials Discovery Engine
Ready now. Use Lattice when materials and energy teams need viable crystal, alloy, coating, electrolyte, or catalyst candidates under real stability and manufacturability constraints.
ReadyMatterSpace Vital
Longevity & Epigenetic Reprogramming Engine
Ready now. Generates candidates for rejuvenation, epigenetic reprogramming, and delivery design under safety and biological plausibility constraints.
ReadyMatterSpace Origin
Protein Generation Engine
Ready now. Use Origin when pharma and industrial teams need viable protein candidates generated under real-world constraints.
ReadyMatterSpace Algo
Algorithm Search Engine
In development. Algorithm search and n-rank search using the same constrained generation pipeline.
In developmentMatterSpace Tessera
Chip Design Engine
In development. Layout-aware chip design generation with validity and design-rule enforcement built in.
In developmentArchitecture
One engine because the design challenge is the same.
Many design problems share the same structure: huge search spaces, hard feasibility rules, and competing objectives. A single engine handles the search; domain-specific science supplies the rules.
Materials science and longevity programs face the same computational problem: a high-dimensional landscape governed by domain rules, a search for viable configurations, and a need for multiple strong options across trade-offs. The engine stays the same. The domain-specific physics and constraints change.
Separate the engine from the science
The reusable engine enforces constraints during generation, explores multi-objective trade-offs instead of collapsing to a single score, and adapts its search strategy automatically.
Category-specific science supplies everything the engine cannot infer on its own: force fields, physical constraints, symmetry groups, objective functions, compositional samplers, and validation criteria specific to each field. New categories require new science layers, not a new engine.
This separation is why Lattice and Vital are ready today, and why new lines can advance on the same architecture.
Core Engine
Domain-agnostic — the same across every field
- Adaptive dynamics controller with real-time strategy selection
- Constraint enforcement during generation, not post-hoc
- Multi-objective optimization across Pareto fronts
- Deterministic replay, provenance, and evidence ledger
Domain Pack
Field-specific — swapped per scientific domain
- Force fields and energy models calibrated to the domain
- Physical constraints (symmetry, bonds, charges, stoichiometry)
- Objective functions and multi-criteria scoring
- Compositional and structural samplers
- Validation tiers, acceptance criteria, and blind benchmarks
Constraint-Aware Generation
Candidate structures are generated in continuous configuration space with feasibility rules enforced at every step.
Physics-Grounded Navigation
Candidates move through dynamics that respect the physical laws of the target domain. Constraint satisfaction is built into the generation process, not applied afterward.
Adaptive Control
The dynamics controller selects the right search strategy in real time based on landscape conditions, so the engine reaches productive regions and stabilizes viable outputs automatically.
MatterSpace is patent pending in the United States and other countries. Vareon, Inc.
Adaptive Dynamics
Every step stays productive.
The adaptive dynamics controller selects the right search strategy in real time. No manual tuning between runs.
Local Refinement
Improves candidates near known stable configurations. The starting point for any campaign where a reasonable initial geometry exists.
Broad Search
Covers regions of the landscape that local methods miss. Finds viable candidates outside the neighborhood of the starting point.
Global Reach
Accesses high-quality configurations separated from the current region by unfavorable intermediate states. Reaches candidates that incremental search cannot.
Output Locking
Commits the final structure to a physically valid, constraint-satisfying state ready for downstream evaluation or synthesis.
Campaign Modes
Four ways to discover
Each campaign mode represents a different level of prior knowledge and evaluation strictness, from open exploration to strict blind rediscovery.
Open Discovery
Greenfield exploration. No reference structure, no target. MatterSpace searches the full compositional and structural landscape under physics constraints. Pure discovery.
Prototype Optimization
Start from a known structure and explore its local neighborhood. Refine compositions, geometries, and properties while staying within the stability basin of the anchor.
Guided Rediscovery
Target structures inform the search. Similarity scoring weights guide generation toward known configurations. Useful for validating the engine against established science.
Blind Rediscovery Benchmark
The strictest mode. Targets are evaluation-only and completely hidden from the generation pipeline. Zero information leakage. The definitive benchmark for engine capability.
The Pipeline
From target to archive
Define what you want. The engine selects from hundreds of parameters, picks the right pipeline, and runs it. Every step is observable.
Define
Agent or human specifies target properties, constraints, and objectives. MatterSpace auto-selects the right science profile, dynamics parameters, and campaign mode.
Generate
Candidate structures are sampled from the domain-specific compositional and structural search space. Initial configurations respect symmetry and stoichiometry constraints.
Navigate
The adaptive dynamics controller drives candidates through the energy landscape, selecting the right strategy in real time.
Enforce
Physical constraints are enforced during navigation, not after. Bond lengths, coordination numbers, symmetry groups, charge neutrality — validated at every step.
Validate
Multi-tier validation. Fast filters eliminate non-viable candidates. Relaxation confirms local stability. Property prediction scores against objectives. High-fidelity verification on top candidates.
Score
Multi-objective scoring across competing properties. Not a single best answer — a diverse archive of Pareto-optimal candidates trading off real-world constraints.
Archive
Every candidate is a typed, provenanced artifact. Full configuration snapshots, dynamics trajectories, constraint satisfaction records, and deterministic replay recipes.
Integration
Connect to the design workflow you already run.
Use the access layer that fits your team, from direct API calls to Python workflows, CLI automation, and MCP-based tools.
How teams use MatterSpace
- 1Define target properties, domain constraints, and the kind of candidate set you need.
- 2MatterSpace selects the right public category, search settings, and scoring objectives for the run.
- 3The campaign returns candidate structures with scores, constraint checks, and provenance records.
- 4Your team ranks the resulting frontier, selects the best options, and launches follow-up runs from what worked.
MatterSpace Lattice
Materials design from target to candidate set.
The first production-ready domain pack, built for teams designing functional materials under real physical constraints.
Battery Cathodes
Ionic conductivity, voltage stability, and thermodynamic ground states for advanced energy storage.
Catalysis
Surface geometries and compositions optimized for target binding energies and reaction pathways.
Superconductors
Crystal structures with target critical temperatures and electronic properties under extreme conditions.
Magnets
Permanent magnet compositions with optimized magnetocrystalline anisotropy and Curie temperatures.
Photovoltaics
Band gap engineering for solar cell absorbers. Direct-gap semiconductors with optimal carrier properties.
Thermoelectrics
Materials with high Seebeck coefficients and low thermal conductivity for waste heat recovery.
High-Entropy Alloys
Multi-principal-element alloys with target mechanical, thermal, and corrosion-resistance properties.
Electrolytes
Solid-state and liquid electrolyte compositions with optimized ionic transport and electrochemical stability.
Coatings
Protective and functional surface coatings with target hardness, adhesion, and environmental resistance.
What makes Lattice different
Symmetry by Architecture
Rotational, translational, and permutational symmetries are respected by architecture, not by data augmentation. Fewer training samples, no spurious symmetry-breaking artifacts.
Valid by Construction
Crystal symmetry groups, bond-length bounds, coordination numbers, and charge neutrality are enforced during generation. Every output is physically valid. No post-hoc filtering required.
Diversity, Not Just Quality
The evolutionary outer loop maintains a structured archive of diverse, high-quality candidates across a Pareto front: ionic conductivity vs. stability, hardness vs. ductility, and other real trade-offs.
Reproducible by Default
Every campaign produces deterministic replay recipes. Configuration snapshots, dynamics trajectories, random seeds, and constraint satisfaction records. Re-run any campaign and get the same candidates.
How We Differ
How this differs from generate-then-filter pipelines.
Feasibility and trade-offs stay inside the generation process. Teams spend more time evaluating strong options, less time discarding invalid ones.
Chemistry AI Labs
- Post-hoc filtering of invalid outputs
- No physics constraint enforcement
- Screening, not discovery
- Single-domain tools
- No provenance or replay
Traditional Computational Labs
- Full physics grounding
- Hours to days per candidate
- No generative capability
- DFT-only, exhaustive enumeration
- Manual provenance tracking
MatterSpace
- Constraints enforced during generation
- Physics-grounded energy landscapes
- Fast generative discovery
- Universal engine, category-specific science layers
- Deterministic replay and full provenance
Engine, not model
Why an engine, not just a model.
Conventional generators produce candidates, then filter out the invalid ones. MatterSpace enforces constraints during generation, optimizes across competing objectives, and produces deterministic, provenanced artifacts.
| Conventional Generators | MatterSpace | |
|---|---|---|
| Generation approach | Generate then filter | Constraint-aware generation with adaptive search |
| Constraint handling | Post-hoc filtering — discard invalid outputs | Enforced during generation (valid by construction) |
| Optimization | Single-objective (stability or one property) | Multi-objective Pareto frontier across competing properties |
| Domain scope | Single domain per model (materials OR drugs OR proteins) | Universal engine — category-specific science layers |
| Diversity | Mode collapse toward reward maximum | Structured archive of Pareto-optimal candidates |
| Reproducibility | Stochastic, seed-sensitive | Deterministic replay with full provenance |
| Model flexibility | Fixed, vendor-provided model | Bring your own, use ours, or build from scratch inside the engine |
| Validation | External DFT verification required | Multi-tier validation built into the pipeline |
Why MatterSpace
Beyond open-source tooling.
Four Campaign Modes
Open Discovery, Prototype Optimization, Guided Rediscovery, and Blind Rediscovery. Each serves a different design intent and level of prior knowledge.
Valid by Construction
Constraints are enforced during generation, not applied as post-hoc filters. Every candidate that exits the engine satisfies your specification.
Multi-Objective Pareto Optimization
Competing objectives handled natively. The engine navigates trade-off landscapes and returns the Pareto frontier — not a single best guess.
Adaptive Dynamics Controller
Selects the right search strategy in real time based on landscape conditions. No manual tuning between runs.
Five Lines
Pre-built constraint sets, force fields, scoring objectives, and samplers. Lattice for materials, Vital for longevity, Origin for proteins, Algo for algorithms, Tessera for chip design.
Bring Your Own Model
Use MatterSpace models or plug in your own. The engine trains your model to bias toward your objectives and goals. The strength is the pipeline, not any single model.
Validation
Blind rediscovery across materials and longevity
Can the engine rediscover known targets without being told what to look for?
In blind rediscovery benchmark mode, targets are completely hidden from the generation pipeline. Zero information leakage. The engine explores from physics alone. Post-hoc comparison reveals whether generated candidates match known stable structures or sealed biological targets.
Lattice has blindly generated candidates that match known materials at the structural level. Vital achieved exact top-1 blind recovery in 3/3 sealed longevity challenge families, confirming that one engine architecture discovers across both domains.
3-Level Rediscovery Protocol
Level A — Performance
Generated candidates match or exceed target property thresholds (ionic conductivity, band gap, magnetization).
Level B — Fingerprint
Structural fingerprints of generated candidates match known materials at configurable similarity thresholds.
Level C — Structure
Atomic-level structural comparison. Root-mean-square displacement below threshold against known crystal structures.
Platform
Your objectives. Your candidates. Your deployment choice.
Define objectives, not datasets
No raw data required. Define target properties, constraints, and objectives. The domain pack supplies the science.
Your model, your choice
Use built-in models, plug in your own, or build one from scratch inside the engine. The engine trains your model toward your objectives. The pipeline is the product.
Deploy on your terms
Cloud-hosted, self-hosted, or air-gapped. Models you build inside MatterSpace are yours.
From the Blog
Latest on MatterSpace
Put the engine on the design problem that matters most.
Start from the properties and constraints you care about. Generate a candidate set your team can use. Five lines span materials, longevity, proteins, algorithms, and chip design.
MatterSpace is patent pending in the United States and other countries. Vareon, Inc.