Abstract
Machine-Learning Predicted Surface Plasmon Resonance In article number 2100052, Chia-Chen Wu, Fei Pan, and Yen-Hsun Su employ genetic-algorithm-based artificial neural networks (GANNs) to coordinate the synthesis parameters with surface plasmon. A well-trained GANN model through machine learning and empirical database yields a precise projection of the surface plasmon wavelength of gold sea-urchin-like nanoparticles (GSNPs) via seed-mediated growth. The optimal fabrication parameters of GSNPs can thus be efficiently specified to meet a desired standard.
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