Crystal structure prediction is a fundamental problem in materials science. Traditional methods take hours. We do it in milliseconds.
To discover a new material, you need to predict its crystal structure—how atoms arrange themselves in 3D space. This is computationally brutal. The search space is effectively infinite. Traditional methods like USPEX use genetic algorithms that take hours per structure.
At that speed, you can screen maybe 100 candidates per year. The space of possible materials is 10^60 combinations. You'll never find the good ones by random search. The entire field has been constrained by compute, not by ideas.
Instead of fighting the complexity of the search space, we navigate it. LoNC treats the energy landscape as a navigable structure, not a random field. We find deterministic paths through chaos.
The key insight: chaotic systems have hidden structure. Stable crystal configurations aren't random—they're attractors in phase space. We built math that finds them directly.
Our data structures are lock-free and wait-free. No mutexes. No contention. Every CPU core works at full speed without blocking. This is why we can run on a laptop and outperform datacenters.
100+ million candidates screened. Breakthrough materials identified across every major constraint facing human civilization.
Multiple candidates with critical temperatures above 25°C at ambient pressure.
Catalysts that convert N₂ → NH₃ at room temperature and atmospheric pressure. Replaces Haber-Bosch.
Electrocatalysts operating within 10mV of thermodynamic minimum. Green hydrogen at fossil fuel prices.
Direct atmospheric CO₂ conversion to jet fuel, ethanol, and ethylene. Carbon-neutral aviation.
Power electronics beyond SiC. 6-8 eV bandgaps, 20 MV/cm breakdown fields. Enables solid-state transformers.
Transformer core materials with 10-40x lower losses than current best. Grid efficiency revolution.
Solid-state cooling/heating materials. No compressors, no refrigerants. 25K temperature swing with a magnetic field.
Solid-state battery electrolytes with liquid-like ionic conductivity. No fires, no dendrites.
Phosphorus recovery from wastewater, nitrate removal to N₂, soil carbon sequestration. Circular economy materials.
Total candidates screened in single overnight run:
Runtime: 8 minutes on a MacBook
Perovskite alternatives with higher efficiency and better stability. Beyond the Shockley-Queisser limit.
Solid-state electrolytes, high-capacity cathodes, safer anodes. The next generation of energy storage.
Cheaper, more efficient catalysts for industrial chemistry. Reduce rare earth dependencies.
Wide-bandgap materials for power electronics. Beyond silicon carbide.
High ZT materials for waste heat recovery. Direct thermal-to-electric conversion.
Ultra-hard materials for cutting tools and wear resistance. Beyond tungsten carbide.
// Initialize the LoNC navigator let navigator = LoncNavigator::new(config); // Define target properties let target = MaterialTarget::builder() .ionic_conductivity(">100 mS/cm") .stability_window(">5V") .elements(["Li", "La", "O", "Cl"]) .build(); // Navigate to optimal structures let candidates = navigator .search(target) .parallel(16) // Use 16 cores .top(1000) // Return top 1000 .collect(); // 0.18 seconds later... for material in candidates { println!("{}: {} mS/cm", material.formula, material.conductivity ); }