Data-efficient and Interpretable Inverse Materials Design using a Disentangled Variational Autoencoder
This paper proposes a semi-supervised, disentangled variational autoencoder approach for inverse materials design that improves data efficiency and interpretability by separating target properties from other material features in a latent space.