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Standard validation protocols in materials machine learning continue to rely on the assumption that training and test data are drawn from the same underlying distribution. This assumption is almost invariably violated in real-world materials datasets because of temporal drift in measurement techniques, compositional biases in database construction, and experimental confounders arising from different laboratories and instruments. This conceptual framework article proposes adversarial validation as a diagnostic tool specifically tailored for materials informatics: a method that trains a discriminator to explicitly detect whether a distribution shift exists between any two datasets, thereby revealing hidden generalization failures that conventional train-test splits and k-fold cross-validation cannot expose. The framework introduces the conceptual foundations of adversarial validation, distinguishes it from adversarial attacks, articulates why the technique is particularly powerful in the small-data, high-dimensional, and physically constrained domain of materials science, and offers a five-component structure for its systematic application—feature-space definition, classifier selection, shift-detection thresholding, localization of driving features, and actionable response rules. By embedding materials-specific domain knowledge into the interpretation of discriminator performance, the approach transforms validation from a passive checkpoint into an active diagnostic that can distinguish temporal shift from compositional bias and experimental confounding. The implications for materials AI practice are immediate and transformative: researchers can now report adversarial validation results alongside standard metrics, trigger targeted dataset augmentation or model retraining when shifts are detected, and document potential sources of distribution mismatch in experimental workflows, ultimately raising the robustness and trustworthiness of property predictions that underpin materials discovery and design.

This review systematically examines the treatment of absence and null results in the materials machine learning literature spanning 2017–2022, drawing exclusively on a curated set of 30 peer-reviewed publications and foundational works that address publication bias, negative findings, and reproducibility challenges in data-driven materials discovery. Through a targeted search strategy across databases such as Web of Science, Scopus, and arXiv using terms including “null result,” “negative result,” “publication bias,” “file drawer,” “failed synthesis,” and “reproducibility” combined with materials informatics keywords, the analysis reveals a persistent imbalance: while successful predictions and syntheses dominate published outputs, systematic documentation of failed predictions, unsuccessful syntheses, null correlations, and abandoned model architectures remains exceedingly rare. What is currently reported tends to be limited to negative outcomes that coincidentally reveal mechanistic insights or contradict high-profile hypotheses, whereas what is systematically unreported encompasses the vast majority of unsuccessful hyperparameter searches, negative active learning campaigns, and non-discoveries that yield no novel materials meeting target criteria. The typology of absence and null results developed here identifies six distinct categories—negative predictive outcomes, null hypothesis non-rejection, failed synthesis, non-discovery, failed replication, and abandoned architecture—each carrying unique implications for scientific progress. The consequences of this non-reporting include severe overestimation of model performance, widespread redundant experimental effort, a false sense of methodological consensus across the field, and slowed overall discovery rates as potentially informative negative signals remain invisible. Ultimately, this review offers concrete recommendations for authors, journals, and the broader community to shift incentives toward transparent reporting of absence, thereby restoring balance to the materials AI literature and accelerating reliable data-driven discovery.

In computational materials engineering, the integration of artificial intelligence (AI) has transformed discovery pipelines from labor-intensive simulations to data-driven infrastructures capable of navigating vast chemical spaces. High-throughput computations and machine learning architectures, such as graph neural networks, have enabled rapid property prediction, accelerating the screening of candidates for applications ranging from energy storage to structural alloys. Yet, this paradigm emphasizes forward modeling—mapping inputs to outputs—often at the expense of mechanistic insight, which requires disentangling causal interactions within atomic-scale dynamics. The conceptual divide between property prediction and mechanistic insight manifests in epistemic tensions: predictive models excel in interpolation but falter in extrapolation, while insight-oriented approaches demand representations that encode not just structural motifs but relational hierarchies across scales. This manuscript introduces the Interpretive Cascade Framework, a systems-level conceptualization that reframes materials AI as a layered cascade of representation, inference, and steering logics. By integrating multimodal data streams with feedback-mediated discovery workflows, the framework elucidates how computational infrastructures can balance predictive efficiency with interpretive depth, mitigating risks of epistemic opacity in closed-loop experimentation. Structural layers delineate data ingestion to hypothesis refinement, incorporating uncertainty propagation as a steering mechanism rather than a mere byproduct. Implications for the field lie in reorienting AI ecosystems toward hybrid discovery logics, where representation learning informs inverse design without sacrificing traceability. This interpretive lens fosters resilient infrastructures, enabling materials science to evolve beyond black-box predictions toward epistemically robust computational paradigms that sustain long-term innovation in data-driven materials engineering.