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The advent of foundation models, large-scale pre-trained architectures adapted from natural language processing paradigms, has permeated computational materials science, promising accelerated discovery through data-driven inference. In materials engineering, these models leverage multimodal datasets encompassing atomic structures, properties, and simulations to enable representation learning across scales. However, inherent conceptual limits arise from the interplay between materials' physical hierarchies—spanning quantum to macroscopic levels—and the inductive biases embedded in pretraining strategies. This manuscript synthesizes recent advancements in machine learning architectures, such as graph neural networks and multimodal integration, within materials informatics ecosystems. It identifies epistemic boundaries where foundation models falter in capturing causality, uncertainty, and domain-specific invariances, potentially leading to misaligned discovery pipelines. To address these, we introduce the Matter Pretraining Boundary Framework (MPBF), a conceptual architecture that delineates layers of data assimilation, representational abstraction, and inference steering to mitigate limits in autonomous materials design. Implications extend to high-throughput computation, inverse design, and simulation-experiment coupling, fostering more robust computational workflows in materials engineering. By interpreting these limits through systems-level dynamics, the framework guides infrastructure trade-offs, enhancing the reliability of data-driven paradigms without empirical validation.

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.