SOLUTIONS
Band gap, elastic modulus, thermal conductivity, and spectroscopy across 73 elements — from first-principles physics at 3,600,000× the speed of DFT. Every result is mechanistically traceable. No ML. No database lookups.
From composition to candidate shortlist.
Electronic structure from composition. Classify metals, semiconductors, and insulators — then rank by target band gap.
Thermal conductivity, Debye temperature, and stability estimates for thermal management applications.
Elastic constants, bulk modulus, and structural stability from first principles.
Predicted IR, UV-Vis, and Raman spectra to validate candidates against target signatures before synthesis.
Define target properties — band gap 1.1–1.5 eV, conductivity above threshold — and get materials that match.
Screen entire composition libraries with exportable shortlists for DFT follow-up or synthesis.
FluxMateria computes material properties from first-principles physics. Every prediction is a deterministic, mechanistic calculation that you can trace, audit, and reproduce.
No neural networks. No fitted coefficients. No statistical correlations. Predictions derive from composition, crystal geometry, and fundamental physics — nothing else. New compositions work on day one.
Every band gap, every elastic modulus, every thermal conductivity value has full provenance. Trace any property back through the physics. Same composition, same output, every time.
DFT-competitive accuracy at screening throughput. Explore entire composition spaces — not individual materials. Screen in seconds what DFT computes in days.
Modular tools for materials discovery and characterization.
313 validated materials across 14 categories. Band gap, effective mass, dielectric constants — from composition.
Learn moreIR, UV-Vis, and Raman predictions with peak assignment and confidence scoring.
Learn moreSet property constraints and find materials that meet your spec across the composition space.
Learn moreTransparent benchmarks against experimental data. Full methodology available on request.
Find semiconductors with a target band gap for photovoltaic applications.
Set target: band gap 1.1–1.5 eV, thermal conductivity > 10 W/mK
Run inverse search across the composition space
Batch-compute properties for all candidate compositions
Sort by Pareto front: band gap vs. thermal conductivity
Predict spectra for top candidates
Decision packet with full mechanistic provenance
Bring your target compositions. We'll show you what FluxMateria predicts.
No account needed. Enter a composition and see property predictions with full mechanistic provenance.
Try it now →Upload a material library and screen the full composition space against your constraints.
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