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Computational materials engineering has undergone a transformative shift with the integration of data-driven methodologies and artificial intelligence, enabling accelerated discovery and design of novel materials. Uncertainty quantification (UQ) plays a pivotal role in this paradigm, addressing inherent variabilities in simulations, experimental data, and model predictions to ensure reliable decision-making in materials development. This review synthesizes recent advancements in UQ methods within computational and data-driven materials engineering, focusing on probabilistic modeling, sensitivity analysis, and Bayesian inference techniques deployed across multiscale simulations and machine learning frameworks. We examine deployment contexts ranging from molecular dynamics to additive manufacturing, highlighting how UQ enhances robustness in property prediction, process optimization, and autonomous discovery systems. By integrating insights from high-impact studies the review delineates a systems-level perspective on UQ infrastructures, emphasizing their role in bridging computational predictions with experimental validation. Key challenges such as computational efficiency and data scarcity are contextualized, alongside opportunities for multimodal integration. Ultimately, this synthesis positions UQ as an essential infrastructure for advancing materials informatics toward industrial applicability, offering a forward-looking outlook on scalable, uncertainty-aware workflows in materials engineering.

Closed-loop systems have become foundational to computational and data-driven materials engineering, integrating automated experimentation, machine learning inference, and orchestration software to compress the design-make-test-analyze cycle. These pipelines rely on continuous flows of data, models, and decisions, yet the mechanisms governing the transfer of decision authority between human experts and autonomous agents remain conceptually underdeveloped. Existing infrastructures emphasize optimization and execution but offer limited interpretive frameworks for how authority is dynamically delegated across epistemic states and pipeline stages. This manuscript presents the Decision Authority Delegation Cascade (DADC) Framework, an original systems-level architecture that formalizes delegated experimentation as a structured cascade of authority transfer. The framework delineates layered pipelines—from data representation through model inference and steering logics to execution and feedback—while emphasizing infrastructure trade-offs in representation fidelity, uncertainty quantification, and delegation thresholds. Synthesizing advances in Bayesian active learning, self-driving laboratories, and orchestration platforms, the DADC Framework interprets authority transfer not as a binary handover but as a continuous, computationally steered process that modulates discovery dynamics. The framework offers interpretive insights into scalable computational ecosystems, highlighting pathways to align human epistemic oversight with autonomous operation and to mitigate bottlenecks in closed-loop materials discovery. Its application reframes infrastructure design around explicit delegation logics, with implications for the next generation of autonomous materials platforms.