In recent years, the application of machine learning in materials science has significantly accelerated the discovery of new materials. In particular, when combined with traditional methods such as first-principles calculations, machine learning models have proven effective in screening potential high-performance materials from existing databases. However, these methods are largely limited by the known chemical spaces, making it difficult to achieve the active design of novel material structures. To overcome this limitation, generative models have become a promising tool for inverse material design, providing new avenues for exploring unknown structures and property spaces. Although existing generative models have achieved initial progress in crystal structure generation, achieving property-guided material generation remains a significant challenge. In this review paper, we first introduce the representative generative models recently applied to materials generation, including CDVAE, MatGAN, and MatterGen, and analyzes their basic abilities and limitations in structural generation. We then focus on strategies for incorporating target properties into generative models to generate the property-guided structure. Specifically, we discuss four representative methods: Con-CDVAE based on target property vectors, SCIGEN with integrated structural constraints and guidance mechanisms, a fine-tuned version of MatterGen leveraging adapter-based property control, and a CDVAE latent space optimization strategy guided by property objectives. Finally, we summarize the key challenges faced by property-guided generative models and provide an outlook on future research directions. This review aims to offer researchers a systematic reference and inspiration for advancing property-driven generative approaches in material design and provides researchers with a systematic reference and insight into the advancement of property-driven generative methods for materials design.
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