Metasurface Optical Filter Performance Predictions from 5,000 Designs

5,000 simulated designs of titanium dioxide metasurfaces for predicting optical filter performance based on geometric parameters. The dataset originates from Kaggle, though the author and specific creation details are not provided. Its intended use is for modeling the relationship between nanoscale structure and optical response.

Use Cases

• Train machine learning models to predict optical filter transmission/reflection spectra based on geometric parameters.
• Perform inverse design to discover metasurface geometries that achieve target optical performance.
• Analyze the relationship between structural features like periodicity or pillar shape and resulting filter behavior.

Strengths

• Contains 5,000 distinct metasurface design instances.
• Focuses on a specific, technologically relevant material (TiO2).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Kaggle

Collection Method

Likely contains simulation data for optical performance based on geometric parameters.