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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.