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近年来，机器学习在材料科学中的应用显著加快了新材料的发现，特别是在结合第一性原理计算等传统方法后，能够高效筛选已有数据库中的潜在高性能材料.然而，此类方法大多局限于已有化学空间，难以实现对全新材料结构的主动设计.为突破这一瓶颈，基于生成模型的材料逆向设计方法逐渐兴起，成为探索未知结构与性质空间的重要手段.尽管当前生成模型在晶体结构生成方面取得了初步进展，但如何实现目标性质导向的材料生成仍面临显著挑战.本文首先介绍了近年来在材料生成领域中具有代表性的生成模型，包括 CDVAE、 MatGAN 以及 MatterGen，分析其在结构生成上的基本能力与局限.随后重点探讨如何将目标性质有效引入生成模型，实现性质导向的结构生成， 具体包括： 基于目标性质向量的 Con-CDVAE、 融合结构约束与引导机制的 SCIGEN、 通过适配器实现性质调控的微调版 MatterGen 以及结合隐空间搜索优化的 CDVAE隐变量优化策略.最后总结当前性质导向生成机制面临的挑战，并展望其未来的发展方向.本文旨在为研究者深入理解和拓展性质驱动的材料生成方法提供系统性参考和启发.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 approaches are largely constrained within known chemical spaces and struggle to enable the active design of entirely novel material structures. To overcome this limitation, generative models have emerged as a promising tool for inverse material design, offering new avenues to explore unknown structural and property spaces. Although existing generative models have achieved initial progress in crystal structure generation, achieving property-guided material generation remains a significant challenge. This review first introduces representative generative models recently applied to materials generation, including CDVAE, MatGAN, and MatterGen, and analyzes their fundamental capabilities and limitations in structural generation. We then focus on strategies for incorporating target properties into generative models to achieve property-directed structure generation. Specifically, we discuss four representative approaches: 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. This work aims to provide researchers with a systematic reference and insight into the advancement of property-driven generative methods for materials design.
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Keywords:
- machine learning /
- generative models /
- inverse design /
- property-guided
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