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The field of materials science has witnessed a transformative shift with the advent of representation learning techniques, particularly for analyzing complex microstructures. This review synthesizes recent conceptual advances in representation learning, including deep neural networks, autoencoders, and vision transformers, applied to microstructure data for tasks such as property prediction, inverse design, and evolution modeling. We explore how these methods extract latent features from high-dimensional microstructure images, enabling efficient computation and discovery of structure-property relationships. However, interpretability remains a significant challenge, as black-box models often obscure the physical meaning of learned representations, hindering trust and scientific insight. We discuss strategies for enhancing interpretability, such as attention mechanisms, heat maps, and post-hoc explanations, drawing from recent studies in alloy microstructures and additive manufacturing. The review highlights the integration of domain knowledge to disentangle representations and address data scarcity issues. By examining case studies in metals, ceramics, and composites, we identify gaps in current approaches, including bias in learned features and limited generalizability across materials classes. Ultimately, this review aims to guide future research toward interpretable representation-learning frameworks that accelerate materials design and foster a deeper understanding of microstructural phenomena.

Geometric deep learning has advanced computational materials science, yet most existing approaches remain constrained by the periodicity assumption inherent to crystalline systems. While crystal-based graph neural networks exploit translational symmetry and periodic boundary conditions, amorphous materials—such as glasses, liquids, polymers, and disordered alloys—lack long-range order, exhibit non-repetitive atomic arrangements, and span widely varying system sizes. This mismatch renders periodicity-dependent methods both theoretically inadequate and practically unreliable for disordered structures. This work identifies three key distinctions—non-periodic graph construction, symmetry limited to E(3) equivariance, and non-physical boundary conditions—which collectively give rise to fundamental challenges, including scaling limitations, loss of transferability, and structural representation imbalances. Through theoretical analysis, the limitations of crystal-oriented graph neural networks in amorphous settings are clarified. To address these issues, a new framework is proposed based on size-extensive, E(3)-equivariant architectures that eliminate periodic assumptions and integrate multi-scale encoding of local, intermediate, and global structure with size-normalized aggregation. This formulation provides a principled foundation for geometric deep learning models tailored to amorphous materials and their applications in energy, optics, and structural systems.

In the evolving landscape of computational and data-driven materials engineering, deep neural networks have emerged as powerful tools for accelerating materials discovery and design. These architectures leverage vast multimodal datasets, high-throughput computations, and representation learning to model complex structure-property relationships in materials systems. However, a fundamental tension arises: as network complexity increases to capture intricate physical phenomena, interpretability diminishes, hindering the extraction of scientific insights essential for advancing materials informatics. This interpretability-complexity paradox poses a significant barrier to integrating deep models into autonomous discovery pipelines, where uncertainty quantification and simulation-experiment coupling demand transparent decision-making. To address this gap, we introduce the Interpretive Complexity Equilibrium Framework (ICEFrame), a novel conceptual structure that conceptualizes the dynamic interplay between model depth, representational fidelity, and epistemic transparency in deep materials networks. ICEFrame delineates layered interactions across data ingestion, architectural scaling, and inference steering, incorporating feedback mechanisms to balance trade-offs without empirical validation. This framework offers interpretive lenses for navigating complexity in graph neural networks and foundation models for science, fostering more robust closed-loop experimentation and inverse design strategies. By reframing the paradox through systems-level insights, ICEFrame implications extend to enhancing discovery steering logics in materials AI, ultimately promoting sustainable innovation in computational materials ecosystems.