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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.

Graph neural networks (GNNs) routinely achieve low errors in formation energy prediction, yet they systematically fail for metastable phases lying above the convex hull. These kinetically accessible materials, critical for batteries, catalysis, and thin-film applications, are severely underrepresented in training data dominated by stable ground states. This failure-mode analysis identifies four interlocking issues: (1) convex hull bias that pulls predictions downward toward stable energies, (2) energy range compression that collapses the predicted dynamic range, (3) local environment extrapolation failures caused by strained or unusual atomic geometries unseen in relaxed training data, and (4) error asymmetry producing consistent underprediction for metastable structures. We describe the origins, signatures, and discovery consequences of each mode, propose simple diagnostic tests for practitioners, and outline targeted mitigation strategies including metastable data augmentation, hull-aware regularization, and physics-informed extrapolation. Current GNN performance on convex-hull benchmarks does not guarantee reliability for metastable discovery. Addressing these failure modes is essential for trustworthy machine-learning-assisted design of functional materials that exist because of their position above the convex hull.