Leveraging generative neural networks for accurate, diverse, and robust nanoparticle design

Abstract

Tandem neural networks for inverse design can only make single predictions, which limits the diversity of predicted structures. Here, we use conditional variational autoencoder (cVAE) for the inverse design of core–shell nanoparticles. cVAE is a type of generative neural network that generates multiple valid solutions for the same input condition. We generate a dataset from Mie theory simulations, including ten commonly used materials in plasmonic core–shell nanoparticle synthesis. We compare the performance of cVAE with that of the tandem model. Our cVAE model shows higher accuracy with a lower mean absolute error (MAE) of 0.013 compared to 0.046 for the tandem model. Robustness analysis with 100 test spectra confirms the improved reliability and diversity of cVAE. To validate the effectiveness of the cVAE model, we synthesize Au@Ag core–shell nanoparticles. cVAE model offers high accuracy in predicting material composition and spectral features. Our study shows the potential of cVAEs as generative neural networks in producing accurate, diverse, and robust nanoparticle designs.