Data from: Deep learning for accelerated all-dielectric metasurface design

• Deep learning has risen to the forefront of many fields in recent years, overcoming challenges previously considered intractable with conventional means. Materials discovery and optimization is one such field. However, the metamaterials community currently lacks access to large datasets for training models. This dataset aims to fill this gap by providing approximately 21,000 simulated spectra for different geometric configurations of all-dielectric metasurfaces. For the geometry of the metamaterial, it is a supercell ADM comprised of four cylindrical unit cells. Each unit-cell cylinder is parameterized by a radius and a height (two parameters), yielding an 8-dimensional parameterization, x, for the geometry of the supercell. We select silicon (Si) for the metasurface material and, with no loss of generality, embed it within a vacuum for faster training set acquisition. ... [Read More]

Total Size

22 files (443 MB)

Data Citation

• Nadell, C. C., Huang, B., Malof, J. M., & Padilla, W. J. (2024). Data from: Deep learning for accelerated all-dielectric metasurface design. Duke Research Data Repository. https://doi.org/10.7924/r44j0qd59

Creator

• Nadell, Christian C.
• Huang, Bohao
• Malof, Jordan M.
• Padilla, Willie J.

DOI

• 10.7924/r44j0qd59

Subject

• Deep learning
• Inverse Design
• Metamaterial

Publication Date

• October 23, 2024

ARK

• ark:/87924/r44j0qd59

Contributor

• Lu, Darui

Affiliation

• Electrical and Computer Engineering

Publisher

• Duke Research Data Repository

Type

• Dataset

Format

• CSV

Funding Agency

• Duke University Energy Initiative
• United States Department of Energy
• Alfred P. Sloan Foundation

Grant Number

• DE-SC0014372

Contact

• Willie John Padilla, willie.padilla@duke.edu, https://orcid.org/0000-0001-7734-8847

License

• Creative Commons CC0 1.0 Universal

Title

• Data from: Deep learning for accelerated all-dielectric metasurface design

License

• http://creativecommons.org/publicdomain/zero/1.0/

Repository

• Duke Research Data Repository