Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints. Here, we review recent advances in generative crystal structure modeling, multimodal learning, and closed-loop design pipelines for crystalline solids. We survey how modern generators learn chemical-structural priors from large databases to enable controllable sampling of periodic structures, and compare leading model classes including variational autoencoders, normalizing flows, autoregressive formulations, and diffusion models. Particular attention is given to how feasibility constraints and physical priors are enforced across the workflow, through representation choices, training objectives, sampling-time guidance, and post-generation screening and relaxation. We also discuss how multimodal learning fuses diverse materials modalities, including crystal structures, thermodynamic, electronic information, microscopy, spectroscopy, processing context, and scientific text, to construct a more universal, transferable representation of chemical space. In addition, diverse inverse-design strategies are examined, particularly those that integrate conditional generation with latent optimization, Bayesian optimization, reinforcement learning, and active learning. Finally, we highlight recurring failure modes, such as surrogate exploitation, diversity collapse, distribution shift, and the stability-synthesizability gap, and outline discovery-grade evaluation practices based on staged reporting of validity, novelty, uniqueness, stability, and cost.

翻译：逆向材料设计将材料发现从正向预测转向在物理约束下有针对性地提出满足目标的候选方案。本文综述了晶体固体逆向设计中生成式晶体结构建模、多模态学习和闭环设计流水线的最新进展。我们调研了现代生成模型如何从大型数据库中学习化学结构先验知识，以实现周期性结构的可控采样，并比较了包括变分自编码器、归一化流、自回归模型和扩散模型在内的主要模型类别。特别关注了如何通过表示选择、训练目标、采样时引导以及生成后筛选与弛豫，在整个工作流中施加可行性约束与物理先验。同时，讨论了多模态学习如何融合晶体结构、热力学、电子信息、显微学、光谱学、加工背景及科学文本等多样化材料模态，以构建更具通用性和可迁移性的化学空间表示。此外，系统考察了多样的逆向设计策略，特别是将条件生成与潜在空间优化、贝叶斯优化、强化学习和主动学习相结合的方法。最后，重点指出了常见的失效模式，如代理模型利用过度、多样性崩溃、分布偏移以及稳定性-可合成性差距，并概述了基于分阶段报告有效性、新颖性、独特性、稳定性和成本的研究级评估实践。