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Materials Band Gap BENCHMARK

This page reports band gap benchmark results only. Scope: 1,048 materials with overall MAE 0.703 eV, including metallic (exp=0) and non-metallic (exp>0) segment reporting.

0.703 eV
Band Gap MAE
across 1,048 materials
0.285 eV
MAE (exp = 0)
461 metallic systems
1.032 eV
MAE (exp > 0)
587 non-metallic systems
1,048
Benchmark Size
single fixed evaluation setup

Methodology

How FluxMateria predicts materials properties

Benchmark Method Summary

Band gaps are computed by the production universal physics engine and evaluated against experimental values using absolute error in eV. Results are reported for the full cohort and key physical subsets.

  • 1,048 materials in the benchmark cohort
  • 461 metallic systems (exp = 0)
  • 587 non-metallic systems (exp > 0)
  • Metric: Mean Absolute Error (MAE), eV

Material Families

III-V Semiconductors

Representative compounds: GaAs, GaN, InP

II-VI Semiconductors

Representative compounds: ZnO, CdTe

TMDs

Representative compounds: MoS2, WS2

Perovskites

Representative compounds: CsPbBr3, SrTiO3

Oxides

Representative compounds: TiO2, SiO2, MgO

Elemental (IV)

Representative compounds: Si, Ge, C, SiC

Benchmark Segment Breakdown

Performance across the full cohort and key physical subsets

Segment N MAE (eV) Notes
All materials 1,048 0.703 Primary benchmark metric
Metallic systems (exp = 0) 461 0.285 Exact-zero handling benchmark
Non-metallic systems (exp > 0) 587 1.032 Semiconductors and insulators

Metrics are reported in eV as mean absolute error against experimental band gaps.

Experimental Band Gap Validation

Representative experimental comparisons from the benchmark cohort

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 from the broader 1,048-material benchmark set.

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

Band gap prediction trade-offs: accuracy, speed, and data dependence

Metric FluxMateria DFT (PBE) DFT (HSE06) ML (CGCNN/MEGNet)
Band gap error 0.703 eV MAE 40-50% underestimation tendency 10-20% typical error band ~0.31-0.33 eV MAE
Speed per query ~1 second Hours to days Days ~1 second
Training data required None None None 60K+ materials
Fitted parameters 0 fitted XC functional choice Mixing parameter Millions
Out-of-domain behavior Physics-grounded extrapolation Recompute required Recompute required Can degrade beyond training domain

Key takeaway: FluxMateria delivers benchmarked band-gap performance at interactive speed with no training-data dependency. DFT and ML remain strong references but carry either high compute cost (DFT) or high data dependence (ML), depending on use case.

Download benchmark package

Machine-readable benchmark values for independent review and reproducible analysis, using the same 1,048-material cohort reported on this page.

Originator: FluxMateria

Materials Band Gap Benchmark

Benchmark summary JSON
Headline metrics, segment MAE values, and representative lowest/highest absolute-error examples.
Download JSON
Row-level benchmark CSV
All benchmark rows with formula, experimental value, prediction, and absolute error fields.
Download CSV

Scope & Limitations

Strengths

  • 1,048-material benchmark with 0.703 eV overall MAE
  • Segment transparency: metallic (exp = 0) and non-metallic (exp > 0) reporting
  • Band gap, effective mass, and dielectric constant predictions
  • Blind validation (v8.1) confirms generalization
  • Fully reproducible — no retraining required

Known Limitations

  • Novel compositions outside current validated formula coverage may require additional derivation and validation
  • Thermal and mechanical properties via separate lattice simulation module
  • Strongly correlated electron systems (Mott-like proxy slice) show MAE 1.53 eV (n=24) vs 0.70 eV overall (n=1,048), and should be validated case-by-case
  • 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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