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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.

The field of computational and data-driven materials engineering has undergone rapid evolution, driven by advancements in high-throughput computational screening, machine learning algorithms, and integrated workflows that accelerate materials discovery. This review synthesizes recent developments in materials informatics, focusing on platforms that enable efficient exploration of vast chemical spaces through automated computations and data analytics. Key areas include the application of graph neural networks and representation learning for property prediction, active learning strategies to optimize experimental feedback loops, and the integration of multimodal datasets for enhanced model accuracy. High-throughput methods have facilitated discoveries in diverse domains, such as superconductors, battery materials, and high-entropy alloys, by combining density functional theory simulations with machine learning surrogates. Autonomous laboratories and closed-loop systems represent a paradigm shift, allowing self-driving experiments that minimize human intervention while maximizing discovery efficiency. Uncertainty quantification plays a critical role in guiding these processes, ensuring reliable predictions amid sparse data. This narrative review structures the landscape into computational ecosystems, workflow integrations, and discovery outcomes, highlighting cross-study synergies. It positions the field at the cusp of scalable, inverse design paradigms, where data-driven insights bridge simulation and experimentation to address grand challenges in materials science.

The advent of data-driven approaches has revolutionized materials engineering, enabling inverse design strategies that prioritize target properties to guide material synthesis and optimization. This review synthesizes recent advancements in machine learning architectures tailored for materials informatics, including graph neural networks and representation learning frameworks that capture atomic-scale interactions and multiscale phenomena. We examine the integration of high-throughput computations with experimental workflows, highlighting closed-loop systems that incorporate active learning and uncertainty quantification to accelerate discovery. Key application domains span energy materials, metamaterials, and catalytic systems, where multimodal datasets facilitate simulation-experiment synergies. By analyzing computational ecosystems, we underscore the shift from forward modeling to inverse paradigms, emphasizing autonomous laboratories that iteratively refine hypotheses through data feedback loops. Challenges in generalizability and data scarcity are contextualized within broader systems integration, offering a cohesive perspective on how these tools reshape materials design. This narrative integrates cross-study insights to propose unified frameworks for scalable, data-centric engineering, bridging theoretical models with practical implementations in computational materials science.