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The field of materials engineering has undergone a profound transformation through the integration of high-throughput computation and data-driven methodologies, evolving from traditional trial-and-error approaches to sophisticated closed-loop systems that accelerate discovery. This review synthesizes recent advancements in computational and data-driven materials ecosystems, focusing on the infrastructure enabling autonomous discovery. Key elements include materials informatics platforms that leverage machine learning for property prediction and inverse design, graph neural networks for representation learning, and high-throughput computational workflows that generate multimodal datasets. We examine the progression from static high-throughput screening to dynamic, closed-loop paradigms incorporating active learning, uncertainty quantification, and simulation-experiment integration. Autonomous laboratories represent a pinnacle of this evolution, where AI orchestrates iterative cycles of hypothesis generation, experimentation, and refinement. The synthesis highlights how these infrastructures bridge computational predictions with experimental validation, fostering inverse materials design and optimizing resource allocation in complex chemical spaces. Challenges in data interoperability and model generalizability are noted, alongside prospects for scalable, self-optimizing systems. Overall, this review positions closed-loop data infrastructures as foundational to next-generation materials engineering, promising accelerated innovation in areas like energy storage, catalysis, and structural materials. By integrating diverse literature, we provide a systems-level perspective on how these tools are reshaping the discovery landscape.

In the evolving landscape of computational and data-driven materials engineering, autonomous experimentation platforms are transforming discovery pipelines by integrating machine learning algorithms with robotic systems to accelerate material synthesis and characterization. These self-driving laboratories operate through closed-loop cycles where data acquisition, model inference, and experimental steering occur without continuous human oversight, raising critical questions about resource allocation mechanisms that ensure efficient, unbiased, and scalable operations. This manuscript addresses a conceptual gap in the governance of such systems: the need for structures that allocate decision rights—encompassing experimental priorities, parameter spaces, and computational resources—absent deliberate intervention. We introduce the Implicit Allocation Governance (IAG) framework, which conceptualizes resource distribution as emergent from layered interactions between data representations, inference engines, and discovery logics, emphasizing epistemic trade-offs and feedback dynamics. By synthesizing recent advancements in Bayesian active learning, reinforcement learning-guided workflows, and multi-agent robotic systems, the framework highlights how governance can arise implicitly through system architectures that balance exploration-exploitation tensions and mitigate representational biases. Implications extend to enhancing the robustness of autonomous materials discovery, fostering interoperability across distributed labs, and informing the design of next-generation computational infrastructures. This work underscores the shift from human-centric deliberation to algorithmically embedded governance, paving the way for more resilient and adaptive materials engineering paradigms.