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Autonomous and semi-autonomous laboratories represent a transformative paradigm in materials science, integrating artificial intelligence, robotics, and high-throughput experimentation to accelerate discovery and optimization processes. This review examines the conceptual foundations of these systems, including closed-loop optimization, machine learning algorithms, and modular hardware architectures. We explore their applications in areas such as alloy development, perovskite synthesis, and nanoparticle engineering, highlighting successes that have reduced discovery timelines from years to days. However, we also critically assess associated risks, including data quality issues, algorithmic biases, ethical concerns in resource allocation, and potential safety hazards from unsupervised operations. Drawing on recent advances, we propose balanced implementation strategies that maximize innovation while mitigating risks. The review underscores the need for interdisciplinary collaboration to realize the full potential of these technologies in addressing global materials challenges.

The convergence of large-scale machine learning with materials engineering is reshaping how new materials are conceived, predicted, and realized. Foundation models—pre-trained architectures that learn generalizable, multimodal representations from expansive datasets—are emerging as the computational backbone for next-generation discovery pipelines. This narrative review synthesizes the computational and data-driven ecosystems that have enabled their rise, drawing on advances in materials informatics, graph-based representation learning, and autonomous experimentation. We trace the progression from early machine learning applications in property prediction to scalable graph neural networks that capture atomic-scale interactions with unprecedented fidelity. High-throughput computation and multimodal data integration have created the knowledge bases necessary for training models that generalize across chemical spaces. Central to this evolution are closed-loop systems, where foundation-like models orchestrate active learning, uncertainty-aware selection, and seamless simulation–experiment feedback. Through an original integrative analysis, we identify recurring architectural principles—such as hierarchical graph convolutions, contrastive pre-training, and multi-task optimization—and training paradigms that balance exploration with exploitation in vast design spaces. These elements collectively address longstanding bottlenecks in inverse design, property optimization, and length-scale bridging. Positioned at the interface of computational infrastructure and autonomous discovery, this review provides a systems-level perspective on how foundation models are poised to compress the materials innovation timeline from decades to months, while maintaining rigorous physical grounding.

Knowledge graphs (KGs) have emerged as a pivotal infrastructure in computational and data-driven materials engineering, enabling structured representation, reasoning, and integration of heterogeneous data for accelerated discovery. By organizing materials data into interconnected entities and relationships, KGs facilitate advanced querying, inference, and machine learning applications across domains such as materials informatics, high-throughput computation, and inverse design. This review synthesizes recent advancements in KG construction from multimodal datasets, including text corpora, biomolecular integrations, and crystalline structures. We examine how graph neural networks and representation learning enhance molecular contrastive learning and pre-training frameworks for improved molecular representations. In the landscape of computational materials ecosystems, KGs support semantic integration and terminology standardization, bridging simulation and experiment through active learning systems and uncertainty quantification. Applications in autonomous laboratories highlight closed-loop discovery, where KGs enable dynamic knowledge propagation and event-sourced provenance management. We provide an original synthesis framing KGs as unifying backbones for data-model-experiment cycles, emphasizing systems-level integration over isolated tools. Challenges in scalability and interoperability are noted, with future directions toward hybrid human-AI workflows. This narrative underscores KGs' role in transforming materials discovery from empirical to predictive paradigms, fostering interdisciplinary convergence in materials science.

Transfer learning has become a cornerstone of computational materials engineering, addressing the fundamental tension between the exponential growth of high-throughput simulation data and the persistent scarcity of high-fidelity experimental labels. By repurposing knowledge encoded in large-scale computational repositories—ranging from density-functional theory (DFT) databases to molecular dynamics trajectories—transfer learning enables accurate property prediction, inverse design, and autonomous discovery even in data-constrained regimes. This review synthesizes the field’s maturation from early domain-adaptation approaches in microstructure informatics to contemporary foundation-model strategies that span inorganic crystals, organic polymers, and hybrid interfaces. We trace the evolution of techniques including graph-neural-network (GNN) pre-training, multi-fidelity fusion, and structure-aware fine-tuning, while highlighting their deployment in closed-loop pipelines that couple simulation with robotic experimentation. Case studies drawn from battery electrolytes, high-entropy alloys, and 2D heterostructures illustrate how hierarchical transfer frameworks achieve chemical accuracy with orders-of-magnitude fewer labels than scratch-trained models. The synthesis reveals a unifying computational workflow: pre-train on universal descriptors, adapt via frozen or low-rank updates, and close the loop through uncertainty-guided active learning. This infrastructure-level perspective underscores transfer learning’s role in transforming materials engineering from a trial-and-error discipline into a predictive, self-optimizing ecosystem.

The convergence of machine learning, high-throughput computation, and autonomous experimentation has transformed materials discovery into an AI-directed process capable of closed-loop, data-driven innovation at unprecedented speed. This narrative review examines the computational and data-driven materials engineering ecosystem, with a specific focus on the governance, regulatory, and institutional oversight frameworks required to steward these capabilities responsibly. We synthesize developments in materials informatics, representation learning, graph neural networks, active learning, uncertainty quantification, and simulation–experiment integration, showing how these tools have enabled autonomous laboratories and inverse design. Particular attention is given to community-driven calls for standards, explainability, and scientific responsibility that have emerged alongside the technology. By integrating technical literature with explicit discussions of data governance, reproducibility, and ethical deployment, we articulate the need for structured institutional oversight models that span standards bodies, regulatory readiness, and multi-stakeholder governance regimes. These models must operate at the infrastructure level—embedding accountability into discovery pipelines rather than retrofitting them. The review positions institutional oversight not as a constraint on innovation but as an essential enabler that ensures AI-directed materials engineering delivers safe, equitable, and societally beneficial outcomes.