这篇综述系统梳理了逆向材料设计中的生成模型与多模态学习，做材料科学或AI驱动的发现研究的团队可以快速了解当前方法、失败模式和评估实践，节省大量文献调研时间。
本文综述了生成模型、多模态学习和闭环工作流在逆向材料设计中的最新进展。逆向材料设计从正向预测转向在物理约束下直接提出满足目标的候选材料。文章比较了变分自编码器、归一化流、自回归模型和扩散模型等主流生成模型，并讨论了如何通过表示选择、训练目标、采样时引导和后生成筛选来施加可行性约束。多模态学习融合晶体结构、热力学、电子信息、显微镜、光谱、加工背景和科学文本，构建更通用的化学空间表示。文章还分析了逆向设计策略，包括条件生成与潜在优化、贝叶斯优化、强化学习和主动学习，并指出了常见的失败模式如替代利用、多样性崩溃、分布偏移和稳定性-可合成性差距。
Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
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.