Generative Models in Inorganic Crystals Discovery and Inverse Design

Discovery of inorganic crystals remains limited by trial‐and‐error experimentation and by the cost of exploring coupled chemical, compositional and structural variables. Generative inverse design addresses this limitation by sampling candidate crystal structures from target properties rather than screening only predefined libraries. Its practical value, however, depends on three technical requirements: a representation that preserves crystallographic information, generative architectures that respect physical constraints, and evaluation protocols that distinguish valid structures from stable and synthesizable candidates. This review examines these requirements for inorganic crystal discovery. We first define the inverse‐design problem and the main generation tasks, then compare variational autoencoders, generative adversarial networks, diffusion models, flow‐based models, large language model pipelines and hybrid frameworks. The comparison shows architecture‐dependent trade‐offs. Variational autoencoders provide continuous latent spaces but often require post‐generation validation; Generative adversarial networks improve distributional sampling but remain sensitive to training instability and mode collapse; diffusion models offer strong conditional control at the cost of iterative sampling and screening; flow‐based methods provide efficient, symmetry‐aware generation; Large language model‐based approaches require standardized and symmetry‐consistent tokens; and hybrid systems assign composition, symmetry, lattice and coordinate generation to separate modules. We then discuss battery and energy materials as application testbeds and review representation choices, data resources and benchmarks. The review identifies physically constrained, multimodal and closed‐loop workflows as the clearest route from generative crystal models to experimentally actionable candidates.