Superconductors & MetaStack
Physics-guided candidate generation, screening, heterostructure design and experimental validation.
GravityIQ.ai develops computational methods, material candidates and sensing technologies at the boundary between established physics, advanced materials science and high-risk frontier research.
The company's primary commercial/scientific objective is materials discovery, beginning with superconductors. Candidate generation is informed by the 8Q/SM8q research framework, conventional condensed-matter physics, numerical simulation and AI-driven search.
Higher-risk work explores signature-control materials and curvature-coupled material systems. Those programs are intentionally evaluated with explicit controls and independent witness measurements rather than treated as established effects.
Generate testable material and physical predictions.
Use conventional solvers, proprietary models and cloud-scale screening.
Use AI to search larger material and parameter spaces efficiently.
Freeze predictions, fabricate candidates and test against controls.
The holding company combines long-duration scientific IP with nearer-term sensing and software assets.
Physics-guided candidate generation, screening, heterostructure design and experimental validation.
AI-assisted computational framework spanning microscopic particle/material problems through large-scale astrophysical tests.
Ferrite-core magnetic-vector sensing, low-SNR recovery, and DF for drone/robot/engine EMI signatures. Approximately 3 km has been demonstrated on ground/engine targets; longer airborne-drone range remains a flight-test objective.
~500k LOC modular RF/EW stack for drones, manpacks, ground vehicles, fixed sites and distributed defense-IoT nodes.
GNSS-jamming and geospatial anomaly analytics using GNSS, AIS/ADS-B and open remote-sensing data, plus separately labeled research into hypersonic-associated and exotic-propulsion signatures.
The public website intentionally stays high-level. Detailed theory, source code, material designs, predictions, experimental protocols and patentable implementations are shared only in controlled technical diligence.