QMS | Physics-Informed Material Intelligence
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Materials Discovery.
Solved In Silico.

The first Physics-Informed AI that predicts battery voltage, superconductivity, and structural stability in milliseconds. Replace trial-and-error with geometric first principles.

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1000x
Faster than DFT
0%
Hallucination Rate
118
Elements Supported
99.5%
LFP Voltage Accuracy

Live Engine Core

v2.4 Stable • Latency: 12ms • IGM-RG Kernel Active

secure://core.iawolf.es/quantum-simulation/active-session

Real-World Validation

Benchmarking IGM-RG™ predictions against industry standards.

Pass
LiFePO4
Cathode Stability
Experimental Voltage3.2V – 3.6V
IGM Prediction3.51 V
Structure IDOlivine (Detected)
99.5% Accuracy
Pass
NbC
Quantum Phase
Material ClassRefractory Ceramic
ConductivityINFINITE
Flux PinningType-II Active
100% Stability
Detected
H2O / Unstable
Safety Check
PredictionCRITICAL FAILURE
Breakdown0.79 V (Electrolysis)
Integrity Score14.3% (Unstable)
Engine correctly flags non-viable candidates before synthesis.

Multi-Industry Applications

One geometric engine. Infinite applications.

Battery & Storage

Predict breakdown voltage, ionic diffusion, and structural integrity of new cathode materials (LFP, NMC, Sodium-Ion) before synthesis.

Aerospace & Defense

Find materials with Zero Weyl Expansion for satellites and hypersonic shields. Predict thermal stability at extreme temperatures.

Semiconductors

Predict dielectric breakdown voltage for high-power chips (SiC, GaN). Detect quantum tunneling and leakage currents in dielectrics.

Ready to accelerate discovery?

Join forward-thinking R&D teams who use QMS to filter the noise.

Research
Free
For academic & non-commercial use.
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ENTERPRISE
Industry
Custom
Full periodic table, API access & IP protection.
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