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In the rapidly evolving field of computational and data-driven materials engineering, AI-guided design has emerged as a transformative paradigm, leveraging machine learning and high-throughput computations to accelerate materials discovery. However, persistent bottlenecks in experimental validation hinder the seamless transition from computational predictions to real-world applications. This conceptual manuscript examines these challenges through a systems-level lens, framing them within the broader materials informatics ecosystem. Key issues include the misalignment between simulation-derived datasets and experimental realities, uncertainty propagation in model inferences, and the inefficiencies in closed-loop discovery pipelines. We introduce the Validation Alignment Network (VAN) framework, an original conceptual architecture that integrates representation learning, uncertainty quantification, and simulation-experiment coupling to mitigate these bottlenecks. By emphasizing epistemic risk structures and computational steering logics, VAN provides interpretive insights into optimizing discovery workflows. Implications extend to enhancing autonomous discovery systems and foundation models for science, fostering more robust AI integration in materials research. This work underscores the need for infrastructure-level advancements to bridge computational predictions with empirical validation, ultimately advancing data-driven materials innovation.

The integration of artificial intelligence, robotics, and high-throughput computation has transformed materials engineering into a domain of autonomous discovery, where self-driving laboratories execute closed-loop experimentation at scales previously unattainable. These systems ingest vast datasets, train predictive models, and steer experimental campaigns toward novel materials with minimal human intervention, promising to compress discovery timelines from decades to months. Yet this autonomy introduces a distinct class of systemic vulnerabilities. Governance failures—misalignments in data integrity protocols, model validation regimes, or decision orchestration logics—do not remain isolated; they propagate through the computational pipeline, amplifying epistemic uncertainties and eroding the reliability of downstream materials outcomes. Existing literature has catalogued the technical foundations of these platforms, from Bayesian optimization in active learning to graph neural networks for property prediction and multi-fidelity workflows. However, a conceptual gap persists: the infrastructure-level dynamics of governance failure propagation remain largely unarticulated within the data-driven materials ecosystem. This manuscript introduces the Cascading Governance Failure Propagation (CGFP) Framework, an original systems architecture that reframes autonomous materials design as a layered computational process governed by interconnected control nodes. The framework elucidates how local misalignments in data curation, inference alignment, and steering logics cascade across pipelines, generating interpretive insights into workflow resilience and infrastructure trade-offs. By positioning governance as an intrinsic computational layer rather than an external overlay, the CGFP Framework offers a conceptual scaffold for designing more robust autonomous discovery ecosystems. Its implications extend to the sustainable scaling of data-driven materials engineering, where failure propagation must be anticipated as a core design constraint.