
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