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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.

The advent of graph neural networks (GNNs) has revolutionized computational materials engineering by enabling sophisticated representations of atomic structures and interactions for property prediction. This review synthesizes key developments in GNN architectures tailored for materials science, focusing on their application in predicting mechanical, electronic, and thermodynamic properties of diverse materials systems, including polycrystals, metal-organic frameworks, and perovskites. Drawing from high-impact studies, we examine the evolution from basic crystal graph convolutional networks to advanced variants incorporating transfer learning, data augmentation, and force field integration. The synthesis highlights how GNNs address challenges in materials data sparsity and structural complexity through graph-based featurization, leading to improved accuracy in property forecasts compared to traditional machine learning methods. We integrate perspectives on GNNs' role in broader data-driven ecosystems, including their synergy with active learning for autonomous discovery pipelines. Limitations such as interpretability and scalability are critically assessed, alongside advances in benchmark frameworks that standardize evaluations. The review positions GNNs as a cornerstone of next-generation materials informatics, accelerating the design of high-performance materials for energy, catalysis, and structural applications. Future outlooks emphasize hybrid integrations with physics-based simulations to bridge experimental and computational gaps, fostering closed-loop systems for rapid materials innovation. This narrative underscores the transformative potential of GNNs in reshaping materials engineering paradigms.

In the evolving landscape of computational and data-driven materials engineering, transfer learning has emerged as a pivotal strategy to address data scarcity and enhance predictive capabilities across diverse materials systems. This approach leverages pre-trained models from one materials class to inform modeling in another, capitalizing on shared representational structures within high-dimensional chemical and physical spaces. However, the conceptual boundaries of reusability remain underexplored, particularly in terms of how representational invariances and domain shifts influence cross-class applicability. This manuscript introduces a novel conceptual framework, termed the Reusability Boundary Architecture (RBA), which delineates the systemic interactions between data representations, model architectures, and discovery workflows in transfer learning paradigms. By integrating insights from materials informatics, graph neural networks, and uncertainty quantification, the RBA elucidates the epistemic trade-offs inherent in transferring knowledge across materials classes, such as from inorganic crystals to organic polymers or metallic alloys to ceramics. The framework emphasizes computational steering logics that dynamically adjust for feature misalignment and contextual divergences, fostering more robust integration of simulation and experimental pipelines. Implications for the field include enhanced design of multimodal datasets, refined autonomous discovery systems, and improved inverse materials engineering, ultimately accelerating innovation in sustainable materials development without relying on empirical validations. This work provides a theoretical foundation for navigating the reusability frontiers in computational materials science, promoting interdisciplinary synergies between machine learning and domain-specific knowledge.

The computational and data-driven paradigm has fundamentally reshaped materials engineering, enabling the navigation of vast chemical and structural spaces through machine learning, high-throughput computation, and autonomous workflows. Yet this transformation has also exposed a critical conceptual gap: the absence of explicit, structured boundaries that govern the division of cognitive labor between human experts and artificial intelligence systems. Without such boundaries, AI contributions risk overstepping domains requiring physical intuition, ethical judgment, and contextual synthesis, while human oversight may inadvertently constrain the scale and speed that define modern discovery pipelines. This manuscript introduces the Epistemic Jurisdiction Framework (EJF), an original systems-level model that delineates jurisdiction layers, interfaces, and feedback mechanisms tailored to the materials discovery ecosystem. The EJF maps the flow from raw data to validated discovery through distinct zones of human primacy, AI autonomy, and negotiated hybrid spaces, emphasizing representation–inference interactions and computational steering logics. Grounded in the recent literature on machine learning for materials, explainable systems, and data-driven infrastructures, the framework offers a conceptual scaffold for infrastructure-level design rather than performance optimization. Its implications extend to the construction of more robust, interpretable, and sustainable computational ecosystems in which human and AI capabilities are aligned rather than blurred. The EJF thereby provides a foundation for next-generation materials engineering platforms that preserve epistemic integrity while fully exploiting computational scale.