AuraOne
Domain Lab

Test a thousand alloys before you touch one.
You screen hundreds of candidates before one reaches the lab. AI predicts properties. You test fewer.
For materials R&D teams that screen hundreds of candidates before one reaches the lab.

Upload CIF structures. AI predicts mechanical, thermal, and band gap properties, runs multi-objective optimization, and correlates against your XRD/tensile results. ISO/NERC evidence included.

Your first run

10 CIF structures. Property predictions in hours. First lab correlation same week.

AI LabsRegression BankControl Center
Accepted formats
CIF / POSCAR / XYZ

Upload crystal structures in formats your DFT tools already export.

Compliance frameworks
ISO / NERC

Audit evidence structured for materials certification and regulatory review.

Pre-built agent
property-predictor

Batch property prediction, multi-objective optimization, and lab correlation.

Focus Areas

What this lab is built for.

Surfaces designed for scientists, operators, and executives. Every output is versioned with provenance so no work is lost.

Property Prediction

Virtual testing ground.

  • Predict mechanical, thermal, and band gap properties from CIF structures
  • Estimate stability, density, and manufacturability early
  • Screen candidates before committing lab time

Multi-Objective Design

Balance strength, weight, cost.

  • Optimize compositions for conflicting targets
  • Suggest new candidates using gradient-boosted models
  • Overlay cost constraints and supply chain factors

Lab Correlation

Closing the simulation gap.

  • Ingest XRD and tensile test results to validate predictions
  • Compare simulation vs. measured outcomes with provenance
  • Export validated pairs as ISO/NERC audit evidence
Signature Workflow

How Materials runs.

Automated evaluation. Expert routing. Regression-tested releases.

Step 1

Upload

Drop your structures.

  • Upload CIF, POSCAR, or XYZ crystal structures for batch analysis
  • Run property predictions against target specifications
  • Filter candidates by stability and manufacturability
Step 2

Optimize

Multi-objective screening.

  • Route top candidates to the lab for physical testing
  • Upload XRD/tensile results to close the simulation-lab gap
  • Visualize simulation vs. reality discrepancies
Step 3

Certify

Export with evidence.

  • Use outcomes to tighten rubrics and regression coverage
  • Enforce promotion criteria via Regression Bank
  • Export ISO/NERC audit evidence for certification

Overview

Upload your crystal structures and get batch property predictions before anything reaches the lab. AuraOne Materials screens candidates for mechanical, thermal, and electronic properties — then correlates predictions against your XRD and tensile results to close the simulation gap.

Materials capsule

You screen hundreds of candidates before one reaches the lab. AI predicts properties. You test fewer.

  • Upload CIF structures. AI predicts mechanical, thermal, and band gap properties, runs multi-objective optimization, and correlates against your XRD/tensile results. ISO/NERC evidence included.
  • CIF / POSCAR / XYZ Accepted formats
  • Focus: Property Prediction
  • Workflow: Upload

Virtual assay

Upload CIF structures and score candidates against your acceptance criteria. Evidence is captured so reviewers can trace how outcomes were produced.

Materials capsule

You screen hundreds of candidates before one reaches the lab. AI predicts properties. You test fewer.

  • Upload CIF structures. AI predicts mechanical, thermal, and band gap properties, runs multi-objective optimization, and correlates against your XRD/tensile results. ISO/NERC evidence included.
  • CIF / POSCAR / XYZ Accepted formats
  • Focus: Property Prediction
  • Workflow: Upload

Evidence loop

As you feed back lab results, your evaluation suite strengthens. ISO/NERC audit evidence is exportable when certification or procurement asks for it.

Materials capsule

You screen hundreds of candidates before one reaches the lab. AI predicts properties. You test fewer.

  • Upload CIF structures. AI predicts mechanical, thermal, and band gap properties, runs multi-objective optimization, and correlates against your XRD/tensile results. ISO/NERC evidence included.
  • CIF / POSCAR / XYZ Accepted formats
  • Focus: Property Prediction
  • Workflow: Upload

Proof hooks

Evidence that stays attached

This is what the system is designed to record for Materials workflows. Metrics and outcomes depend on your deployment and must be validated in staging and production.

ISO / NERC
Audit evidence structured for materials certification and regulatory review.
Lab correlation
Simulation predictions linked to XRD and tensile test results.
Quality gates
Promotion rules that prevent unvalidated candidates from advancing.
Want the security and evidence model? Start in the Trust Center.
Visit Trust Center

Start your Materials Lab.

One session. First lab running.