Published April 22, 2026 | Version v1
Dataset Open

Dataset 'Advancing Machine Learning Optimization of Chiral Photonic Metasurface: Comparative Study of Neural Network and Genetic Algorithm Approaches'

Description

This dataset contains the shapes and spectra generated through the use of the chiral metasurface machine-learning optimization framework in the article "Advancing Machine Learning Optimization of Chiral Photonic Metasurface: Comparative Study of Neural Network and Genetic Algorithm Approaches". A preprint of the article is available on arXiv via external link [ arXiv:2512.13656, https://arxiv.org/abs/2512.13656 or https://doi.org/10.48550/arXiv.2512.13656 ]. Also, see accepted manuscript https://doi.org/10.1002/apxr.202500223 (Advanced Physics Research).

Files

GA_fitness_score_evolutions_data_Filippozzi_et_al_paper.zip

Files (107.5 MB)

Additional details

Related works

Is supplement to
Preprint: arXiv:2512.13656 (arXiv)

Funding

Deutsche Forschungsgemeinschaft
DFG RA2841/12-1 456700276
Fund for Scientific Research
Plateforme Technologique de Calcul Intensif (PTCI) Convention No. 2.5020.11
Sino-German Center for Research Promotion
CDZ GZ1580: Sino-German Cooperation Group ("FNMS-COOP") GZ1580

Dates

Collected
2025
Simulated optical properties and machine-learning data from genetic algorithm (GA) and neural network (NN) pipeline
Collected
2026
Simulated optical properties and machine-learning data from genetic algorithm (GA) and neural network (NN) pipeline