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Generative models have emerged as transformative tools in materials science, enabling the inverse design of novel materials with tailored properties by learning from vast datasets of structures and compositions. This review synthesizes recent advancements in generative approaches, including variational autoencoders, generative adversarial networks, diffusion models, and large language models. It highlights their conceptual capabilities for accelerating discovery while addressing scientific limits such as data scarcity, synthesizability, and interpretability. By examining applications in inorganic crystals, organic molecules, and energy materials, we delineate how these models bridge computational efficiency with experimental validation, yet face challenges in generalizability and physical fidelity. Future directions emphasize hybrid physics-informed architectures and closed-loop automation to overcome current barriers and unlock sustainable materials innovation.

Machine learning has transformed materials engineering through graph neural networks, equivariant architectures, generative models, and early autonomous laboratories. These advances enabled accurate property prediction, data-efficient force fields, and initial inverse design of inorganic crystals, supported by community databases such as the Materials Project and JARVIS. Generative frameworks now propose novel structures, while closed-loop platforms demonstrate early integration of prediction, synthesis, and robotic experimentation. However, major gaps remain: weak extrapolation beyond training distributions, inadequate handling of long-range interactions, poorly calibrated uncertainty quantification, limited synthesis prediction, underrepresentation of disordered materials, and insufficient experimental validation. Persistent unlearned lessons include benchmark biases that inflate generalization performance, poor reproducibility practices, misuse of invariant models for tensor properties, omission of random baselines in active learning, weak validity filtering in generative workflows, and near-absent failure reporting. This review critically assesses a decade of progress and shortcomings, grounded in 38 peer-reviewed publications. It highlights that hype around foundation models, generative inverse design, and autonomous labs often exceeds demonstrated impact. Realizing the field’s potential requires prioritizing extrapolation-focused architectures, mandatory experimental validation, shared failure registries, and stronger evidentiary standards to accelerate reliable discovery of functional materials.