A Unified Framework for Joint Crystal Generation and Property Prediction

Crystal generation and property prediction are two fundamental tasks in the discovery of novel materials. Recently, AI-based frameworks have shown substantial promise for both tasks; however, they have largely evolved as separate paradigms, since each requires distinct representations and inductive biases. Predictive models typically rely on local, neighborhood-based multigraph representations, whereas generative models often operate on global three-dimensional crystal representations. In this work, we investigate whether these complementary representations can be unified to jointly support crystal generation and property prediction. To this end, we propose CrysUniNet, a unified framework that learns a shared embedding space by integrating local and global representations of crystal structures, enabling a single latent representation to support both generative and predictive tasks. We first train a VAE with a unified encoder and a multi-task decoder that maps crystals into this latent space, after which a latent diffusion model learns to generate new latent embeddings. Once trained, CrysUniNet can generate novel crystal materials via the latent diffusion model, while the VAE's property-prediction head enables property prediction for any target material. Empirically, CrysUniNet performs strongly on both tasks, generating $39.15\%$ more SUN crystals than the strongest SUN baseline while also predicting the properties of existing structures more accurately.