Machine Learning-Driven Optimization of Monolithic Gold Plasmonic Sensors

This dataset supports the study “Machine Learning-Driven Optimization of Monolithic Gold Plasmonic Sensors.” It contains simulation-generated and processed data used to train, validate, and test machine learning models for optimizing the structural and optical parameters of monolithic gold plasmonic sensors. The dataset includes refractive index (ri) values, real and imaginary values of x and y axes for different wavelength, loss curve and sensitivity. All data were generated through numerical simulations and systematically curated to ensure consistency and reproducibility. This dataset may be used for research, validation, and comparative studies related to plasmonic sensor optimization and data-driven photonic design.