Material Validation Engine
Cross-check a material prediction against literature and historical data.
by Aigarth Validate
Cross-check a material prediction against literature and historical data. Returns a confidence score, the literature citations, and the historical mean ± σ. Catches predictions that are numerically correct but physically wrong.
Deploy is a Phase 0 stub. Real per-stage cost, MLIP surrogate, and on-chain attribution ship in Phase 2.
What it does
One sentence. The capability the ANN exposes, and the one thing it does really well.
Score a prediction 0–1.0 against literature + historical data, with citations.
At a glance
The numbers the marketplace uses to rank this ANN. All metrics are placeholders until real benchmarks are wired in Phase 1.
Phase 0 stub benchmark
On a single CPU worker
Billed via the platform credit balance
Locked in Qearn to unlock access
Live metrics appear once deployed
Creator share + staker share + protocol fee
Try it
A realistic example input. The output you see on this page is from a Phase 0 stub backend. The format, latency, and cost are real, the data is illustrative.
{
"prompt": "Prediction: band_gap = 2.8 eV ± 0.4, material: LiNi0.5Mn0.5O2, source: mat-simulation-runner."
}{
"confidence": 0.72,
"literature_match": true,
"prior_measurements": 14,
"citations": [
"J. Electrochem. Soc. 2024",
"Nat. Mater. 2023"
]
}Real engine integration ships in Phase 2. The wire format here is what the real response will look like.
Pricing
Three ways to use this ANN. Per-call is the default; reservations give you a discount; custom is for dedicated capacity.
Try the ANN with a 1,000-call reservation. No commitment.
Pre-purchased capacity. 10% discount on per-call pricing.
Private deployment, dedicated worker, custom SLAs.
Anyone can use, modify, and redistribute. Free of charge.
Related ANNs
Other ANNs in the marketplace that share tags with this one.
Plan a material discovery workflow from a research question.
Ingest open-access material science papers into a structured knowledge graph.
Run DFT, MD, or ML surrogate simulations for material properties.
Version history
Every revision is a separate ANN with its own benchmarks. The current version is what's served by default.
| Version | Date | Status | Notes |
|---|---|---|---|
| v1.0.0 | Aug 2, 2026 | current | Initial release (Phase 0 stub backend). |
| v0.9.0 | Jul 24, 2026 | beta | Internal preview. Used for 1,000-paper ingestion benchmark. |
| v0.5.0 | Jul 12, 2026 | deprecated | First preview. Performance baseline. |
Real version history wired in Phase 1. For now, this is a static manifest.
Try Material Validation Engine
This is one of the 8 material science ANNs in the Aigarth marketplace. Stake to participate in the network, and in the revenue when a discovery ships.
Deploy is a Phase 0 stub. Real per-stage cost, MLIP surrogate, and on-chain attribution ship in Phase 2. The dashboard at localhost:4000/material-science is the source of truth for what is real today.