Uncovering the geometry-dependent optical asymmetry of gold nanorods helical assemblies using artificial neural networks

The optical asymmetry of gold nanorods (Au-NRs) helical assemblies is well-documented with a wide range of applications. Nevertheless, the geometry-dependent optical asymmetry within these assemblies has not been adequately explored and quantified. The present study proposes a novel approach to predict the optical asymmetry of Au-NRs helical assemblies based on geometric characteristics using artificial neural networks (ANN). The performance of the ANN termed $3N_{HL}50N_N$ was significantly enhanced through the optimization of the hidden layer and node, resulting in an R² of the outcomes exceeding 0.998 and a reduction in computational time exceeding 99.99 %. In instances where the specific geometric characteristics are needed to attain a desired optical asymmetry, a retrieval of geometric characteristics of Au-NRs helical assemblies was additionally investigated using a traversing mechanism featured particle swarm optimization (PSO) algorithm. The results of the retrieval were obtained within 6 s and demonstrate a high degree of accuracy and reliability. The combination of the $3N_{HL}50N_N$ and the PSO algorithm is capable of accurately predicting the optical asymmetry of Au-NRs helical assemblies and the retrieval of the geometry characteristics, thereby enabling the quantitative understanding of their overall geometry-dependent optical asymmetry.