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Materials AI systems have become indispensable for accelerating discovery in solid-state materials, energy storage compounds, and functional alloys. Yet, they operate without systematic mechanisms to trace the full chain of data provenance, model decisions, and reasoning steps that produce any given prediction or recommendation. The absence of scientific audit trails means that when a novel perovskite composition is proposed, or a predicted bandgap deviates from experiment, researchers cannot reliably reconstruct the exact sequence of data transformations, hyperparameter choices, feature selections, or failure modes that led to the outcome, undermining reproducibility, error diagnosis, and collective scientific progress. This paper proposes a comprehensive blueprint for scientific audit trails tailored specifically to the unique requirements of materials AI workflows, where heterogeneous data sources, multiscale simulations, and iterative human–machine interactions demand far more than generic machine-learning logging. The blueprint defines a machine-readable yet human-accessible record that captures every relevant element of a materials discovery pipeline. Its seven core components—ranging from granular data provenance to detailed failure logs and environmental context—provide the structural foundation for traceability. Four operational principles ensure that capture is automatic, standardized, immutable, and accessible. At the same time, five success criteria establish objective benchmarks for completeness, traceability speed, reproducibility power, error localization precision, and acceptable computational overhead. Finally, a five-phase implementation path offers the materials AI community a practical route from standards development to journal-mandated adoption and AI-assisted analysis. By closing this critical gap, the proposed scientific audit trails will transform materials AI from opaque black-box engines into transparent, accountable scientific instruments.

Reproducibility has emerged as a critical bottleneck in data-driven materials engineering, particularly as graph neural networks (GNNs) and associated uncertainty quantification (UQ) methods are increasingly embedded in high-stakes discovery pipelines. While advances in conformal prediction, Bayesian inference, and ensemble techniques have improved predictive reliability, their practical deployment remains constrained by fragmented workflows, opaque data provenance, and inconsistent reporting standards. This review reframes uncertainty-aware materials modeling through the lens of reproducible workflows, tracing the pipeline from dataset construction and curation to model training, calibration, and deployment. Drawing on peer-reviewed studies, we integrate methodological advances in UQ with emerging practices in data governance, experiment tracking, and model documentation. The analysis reveals that uncertainty estimates are only as trustworthy as the workflows that generate them: biases in dataset composition, undocumented preprocessing steps, and inconsistent calibration protocols systematically undermine reliability, even when state-of-the-art UQ methods are applied. We argue that reproducibility must be treated as a first-class design constraint, requiring standardized data provenance tracking, version-controlled training pipelines, and model cards that explicitly document uncertainty behavior, calibration performance, and failure modes. By linking UQ theory with reproducible systems design, this work establishes a framework for trustworthy materials graph learning in which uncertainty is not merely computed but auditable, interpretable, and transferable across applications.