Search

In the evolving landscape of computational and data-driven materials engineering, the integration of high-throughput simulations, machine learning models, and autonomous discovery systems has accelerated materials innovation. However, the complexity of these pipelines often obscures the origins and transformations of data, leading to challenges in reproducibility, error propagation, and epistemic accountability. This conceptual manuscript addresses the critical need for robust data lineage and scientific traceability mechanisms within computational materials workflows. We introduce a novel framework, the Integrated Traceability Architecture (ITA), which conceptualizes traceability as a multilayered system embedding provenance tracking across data generation, model training, and discovery iterations. By synthesizing recent advancements in materials informatics, representation learning, and uncertainty quantification, the framework elucidates how lineage-aware pipelines can enhance decision-making in inverse design and closed-loop experimentation. Implications extend to fostering reliable multimodal datasets, optimizing simulation-experiment couplings, and mitigating risks in foundation models for materials science. This work provides a systems-level perspective on traceability, promoting infrastructure designs that balance computational efficiency with scientific integrity, ultimately steering towards more transparent and accelerated materials discovery paradigms.