  In recent years, progress has been made in generating new crystalline
materials using generative machine learning models, though gaps remain in
efficiently generating crystals based on target properties. This paper proposes
the Con-CDVAE model, an extension of the Crystal Diffusion Variational
Autoencoder (CDVAE), for conditional crystal generation. We introduce
innovative components, design a two-step training method, and develop three
unique generation strategies to enhance model performance. The effectiveness of
Con-CDVAE is demonstrated through extensive testing under various conditions,
including both single and combined property targets. Ablation studies further
underscore the critical role of the new components in achieving our model's
performance. Additionally, we validate the physical credibility of the
generated crystals through Density Functional Theory (DFT) calculations,
confirming Con-CDVAE's potential in material science research.
