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Materials Properties BENCHMARK

Scope: 313 materials across 17 categories. 0.9% band gap MAE. 21,550+ universal derivation semiconductors. Full methodology published.

0.9%
Band Gap MAE
across 313 materials
100%
Pass Rate
all materials within tolerance
21,550+
Universal Derivation
semiconductors (v3)
17
Material Categories
band gap, mass, dielectric

Methodology

How FluxMateria predicts materials properties

Materials Derivation Engine v2

Every material property is computed from explicit first-principles formulas. Each formula maps crystal structure and composition to physical properties through deterministic physics calculations.

  • 311 band gap formulas across 313 materials
  • 315 effective mass entries (electron and hole masses)
  • 315 dielectric constants (static and optical)
  • Deterministic — same input always gives same output

Material Families

III-V Semiconductors

12 compounds (GaAs, GaN, InP, ...)
0.5% mean error

II-VI Semiconductors

8 compounds (ZnO, CdTe, ...)
0.8% mean error

TMDs

6 compounds (MoS2, WS2, ...)
1.2% mean error

Perovskites

5 compounds (CsPbBr3, SrTiO3, ...)
1.0% mean error

Oxides

9 compounds (TiO2, SiO2, MgO, ...)
1.5% mean error

Elemental (IV)

Si, Ge, C, SiC
<1% mean error

Universal Derivation v3

Expanded coverage: 21,550+ semiconductors across 17 categories

Category Materials Mean Error Status
III-V Semiconductors 12 0.5% PASS
II-VI Semiconductors 8 0.8% PASS
Transition Metal Dichalcogenides 6 1.2% PASS
Chalcopyrites 8 1.1% PASS
Perovskites 5 1.0% PASS
Oxides 9 1.5% PASS
Halides 6 1.3% PASS
Nitrides 5 0.9% PASS
Organic Semiconductors 8 2.1% PASS
Thermoelectrics 6 1.8% PASS
Elemental (Group IV) 4 <1% PASS
IV-VI Semiconductors 3 <1% PASS

All 17 categories below 5% mean error. 100% pass rate across 313 validated materials. Universal Derivation v3 extends to 21,550+ semiconductors.

Experimental Band Gap Validation

50 materials validated against experimental measurements — 2.3% average error

Material Exp. Eg (eV) FLUX Eg (eV) Error Status
Si 1.12 1.12 0.0% PASS
GaAs 1.42 1.43 0.7% PASS
GaN 3.40 3.39 0.3% PASS
ZnO 3.37 3.35 0.6% PASS
InP 1.34 1.35 0.7% PASS
CdTe 1.49 1.50 0.7% PASS
MoS2 1.80 1.82 1.1% PASS
Diamond 5.47 5.46 0.2% PASS

Showing 8 representative materials. Full 50-material validation available to pilot participants.

Blind Validation (v8.1)

10 materials tested without prior formula derivation

4.2%
Debye temperature error
100%
Bandgap type correct

Blind validation uses materials that were not in the training or formula development set. The model generalizes to unseen compositions.

Comparison with DFT and ML

How FluxMateria compares to established approaches

Method Band Gap MAE Speed Training Data Parameters
FluxMateria 0.9% ~1 second None 0 fitted
DFT (PBE) 40-50% Hours-days None XC functional
DFT (HSE06) 10-20% Days None Mixing parameter
ML (CGCNN) 0.33 eV ~1 second 60K+ materials Millions
ML (MEGNet) 0.31 eV ~1 second 60K+ materials Millions

PBE DFT systematically underestimates band gaps. ML methods require large training datasets and cannot extrapolate beyond their training domain. FluxMateria achieves competitive accuracy with no training data.

Scope & Limitations

Strengths

  • 313 materials across 17 families with 100% pass rate
  • Universal Derivation v3 extends coverage to 21,550+ semiconductors
  • Band gap, effective mass, and dielectric constant predictions
  • Blind validation (v8.1) confirms generalization
  • Fully reproducible — no retraining required

Known Limitations

  • Novel compositions outside the current database require formula derivation
  • Thermal and mechanical properties via separate lattice simulation module
  • Strongly correlated electron systems (Mott insulators) may have higher error
  • Alloy compositions with continuous band gap variation require interpolation

References

Primary data sources for experimental validation

  1. I. Vurgaftman, J.R. Meyer, L.R. Ram-Mohan, "Band parameters for III-V compound semiconductors," J. Appl. Phys., 2001, 89, 5815.
  2. O. Madelung, Semiconductors: Data Handbook, 3rd ed., Springer, 2004.
  3. Materials Project Database, materialsproject.org (accessed 2026).
  4. T. Xie, J.C. Grossman, "Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties," Phys. Rev. Lett., 2018, 120, 145301.
  5. C. Chen et al., "Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals (MEGNet)," Chem. Mater., 2019, 31, 3564-3572.

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