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Generative models have emerged as transformative tools in materials science, enabling the inverse design of novel materials with tailored properties by learning from vast datasets of structures and compositions. This review synthesizes recent advancements in generative approaches, including variational autoencoders, generative adversarial networks, diffusion models, and large language models. It highlights their conceptual capabilities for accelerating discovery while addressing scientific limits such as data scarcity, synthesizability, and interpretability. By examining applications in inorganic crystals, organic molecules, and energy materials, we delineate how these models bridge computational efficiency with experimental validation, yet face challenges in generalizability and physical fidelity. Future directions emphasize hybrid physics-informed architectures and closed-loop automation to overcome current barriers and unlock sustainable materials innovation.

The integration of artificial intelligence (AI) and machine learning (ML) in materials science has revolutionized traditional approaches to material discovery, design, and application. This narrative review explores how AI models not only predict material properties but also influence scientific decision-making by providing actionable insights, optimizing experimental strategies, and enabling inverse design paradigms. Drawing on recent advancements, we examine the transition from data-driven prediction to AI-assisted decision-making, highlighting case studies in porous materials, optoelectronics, and polymeric membranes. The review addresses challenges such as data scarcity, model interpretability, and integration with experimental workflows, while proposing future directions for AI to enhance human decision-making in materials research. Ultimately, AI is positioned as a collaborative tool that augments scientific intuition, accelerating innovation in sustainable and high-performance materials.

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.

In goal-directed materials optimization powered by artificial intelligence, researchers routinely employ teleological language such as “target properties,” “design objectives,” and “optimal structures,” implicitly assuming that materials evolve toward purposes or that optimized outcomes represent intended final causes. Scientific teleology, defined here as the explanatory practice of invoking goals, purposes, or final causes as causal factors within material systems that lack inherent intentionality, constitutes a distinct conceptual failure mode in artificial-intelligence-driven materials science. This failure arises through three primary mechanisms—reification of goals, retrospective teleology, and purpose projection—that systematically distort the epistemic relationship between human-specified objectives and the contingent structure–property relationships uncovered by optimization algorithms. The present analysis articulates a typology of four specific teleological failure modes: teleological overclaim, design-versus-discovery conflation, objective naturalization, and teleological explanation. Detection principles based on language audits, objective genealogy, counterfactual testing, and agency attribution enable researchers to identify these assumptions before they propagate, while five mitigation principles—explicit objective contextualization, literal-versus-metaphorical clarity, multiple-objective transparency, avoidance of agency language, and consistent design-versus-discovery distinction—provide practical safeguards. By treating scientific teleology as an identifiable failure mode rather than an innocuous heuristic, the materials artificial-intelligence community can preserve the epistemic integrity of discovery processes and prevent the misinterpretation of optimized materials as possessing purposes they do not inherently possess.

The ambiguous use of “falsifiability” in materials AI literature poses a significant challenge to the scientific status of AI-generated claims, as researchers frequently present predictive or generative outputs—such as “this perovskite structure is stable at room temperature” or “this inverse-designed alloy exhibits a target bandgap of 1.8 eV”—without clarifying whether these statements could, in principle, be contradicted by empirical observation. Rooted in Karl Popper's philosophy of science and extended through contemporary applications to machine learning, falsifiability serves as the demarcation criterion that distinguishes scientific claims from non-scientific ones by requiring that they logically forbid certain observations rather than merely accommodate data. This paper proposes precise definitions for falsifiable, verified, and testable AI-generated materials claims, tailored specifically to the challenges of data-driven discovery in solid-state systems, generative models, and inverse design. It further introduces a four-component framework for assessing the falsifiability of such claims, centering on claim specification, forbidden observation specification, test design, and falsification protocol. These conceptual foundations carry profound implications for materials AI practice, requiring authors to articulate disconfirming evidence explicitly, reviewers to demand falsifiability statements, and the broader community to adopt standards that elevate predictive modeling from statistical correlation to genuine scientific inquiry. By confronting the boundary between data-driven heuristics and empirically falsifiable science, the present work offers a definitional scaffold that can guide the field toward greater epistemic rigor amid the accelerating integration of artificial intelligence into materials discovery.

Generative models for materials inverse design, including variational autoencoders, generative adversarial networks, and diffusion models, have rapidly emerged as powerful tools for proposing crystal structures conditioned on target properties. Yet, despite significant methodological progress, evaluation practices remain fragmented and inconsistent, limiting comparability and obscuring genuine scientific advancement. This review introduces a unified five-dimensional taxonomy—validity, novelty, diversity, property optimization, and stability—and uses it to diagnose systematic distortions in current reporting. Across the literature, metrics are defined heterogeneously, diversity is frequently omitted, stability is selectively assessed, and baseline comparisons are often absent. These inconsistencies inflate reported performance and prevent reliable benchmarking. In response, a minimal reporting standard is proposed, requiring transparent multi-metric evaluation, explicit dataset and split disclosure, baseline-controlled property assessment, distribution-level reporting, and computational-cost transparency. Establishing such community-agreed protocols is essential for transforming generative materials research into a reproducible, comparable, and cumulatively advancing field.

"Novel material" has become the central claim in nearly every paper introducing generative models for materials discovery. Yet the term is used with striking ambiguity. Authors routinely assert that their models have produced "novel materials" without clarifying whether this means the output lies outside the training distribution, satisfies basic chemical rules such as charge balance and realistic bond lengths, or meets the far stricter requirement of being synthesizable in a laboratory. This boundary/definitional article identifies three distinct meanings—distributional novelty, chemical validity, and synthesizability—and demonstrates how the current literature routinely conflates them. A generated crystal may be distributionally novel (absent from the training set) yet chemically invalid; it may be chemically valid yet lie far above the convex hull and therefore remain unsynthesizable. Such conflation leads to overclaims that inflate expectations and hinder reproducible progress in inverse design. This analysis maps the boundary conditions for each meaning and proposes a hierarchical operational definition with six explicit levels. The framework requires authors to report novelty percentages at every level rather than a single vague statistic. Distributional novelty marks the first filter, chemical validity the second, and synthesizability the third, with synthesizability itself subdivided into thermodynamic, dynamical, kinetic, and experimental realizability layers. The article further examines boundary cases, gray zones, and implications for model evaluation and benchmark design. By replacing ambiguous rhetoric with precise, multi-level reporting, the proposed definition establishes a shared language for generative models in materials engineering and prevents the overinterpretation of computational outputs as laboratory-ready discoveries. Adoption of this operational framework will sharpen claims, improve comparability across studies, and ultimately accelerate the translation of generative predictions into experimentally validated materials.

Materials machine learning has achieved remarkable success in building accurate predictive models for properties such as formation energy, band gap, and mechanical strength. Yet the ultimate purpose of the field is not prediction but design: the discovery of new materials that deliver target properties under real-world constraints. This perspective argues that the community must shift from prediction-centric to optimization-centric workflows. Prediction-centric approaches focus on minimizing mean absolute error on randomly held-out test sets, while optimization-centric workflows aim to maximize (or minimize) a target property value within practical budgets, constraints, and sequential decision-making. These are fundamentally different objectives that demand different methods, evaluation metrics, and research priorities. A prediction-centric model may achieve low error on interpolation tasks yet fail catastrophically when asked to guide the search for extreme or out-of-distribution materials. In contrast, optimization-centric workflows treat the machine-learning model as a surrogate within an iterative loop that actively selects the next experiment or simulation. Four core principles underpin this shift: explicit goal specification that includes both the objective and all relevant constraints; robust constraint handling that distinguishes hard feasibility requirements from soft trade-offs; uncertainty awareness, where every prediction is accompanied by well-calibrated uncertainty estimates essential for balancing exploration and exploitation; and closed-loop integration that connects the model directly to automated or high-throughput experimentation and simulation. Adopting these principles will require new evaluation protocols that measure the best material discovered, sample efficiency, regret, constraint satisfaction, and extrapolation distance rather than isolated accuracy metrics. The implications extend to model development, benchmark design, journal standards, and funding priorities. Only by embracing optimization-centric workflows can materials machine learning fulfill its promise of accelerating discovery and delivering materials that solve pressing societal challenges.

Generative materials models promise to accelerate discovery by systematically exploring vast regions of chemical space, yet the core concept of “chemical space coverage” remains poorly defined and inconsistently applied across the literature. Researchers routinely claim that their models achieve high “diversity” or “coverage,” but these statements rest on incompatible assumptions about what chemical space actually encompasses. This boundary/definitional article clarifies the term by distinguishing three primary dimensions—compositional space, structural space, and property space—and demonstrates how current diversity metrics conflate or ignore these dimensions, rendering cross-study comparisons unreliable. Drawing on recent advances in generative modelling for crystals and molecules, the analysis shows that a model may report excellent elemental coverage while entirely neglecting novel crystal prototypes or property combinations, or conversely achieve broad structural diversity within a narrow compositional slice. To resolve these ambiguities, the article proposes an operational definition of chemical space coverage built around four explicit, computable metrics: compositional coverage (), structural coverage (), property coverage (), and joint coverage (). Each metric is accompanied by practical boundary conditions that define thresholds for “broad,” “comprehensive,” or “exploratory” coverage. The framework further articulates five essential boundary conditions for sufficiency—task dependence, reference dependence, sparsity adjustment, validity trade-off, and diminishing returns—thereby transforming coverage from a vague aspirational term into a precise evaluative criterion. Adoption of this multi-dimensional framework will enable consistent benchmarking, prevent over-optimistic claims, and guide the responsible development of generative models that truly expand the frontiers of materials design rather than merely resampling known regions. The proposed definitions and boundaries therefore constitute a necessary foundation for the next generation of inverse design methodologies in computational materials engineering.

The field of materials science has undergone a transformative shift with the integration of computational and data-driven approaches, particularly through representation learning techniques that enable efficient handling of complex materials data. This review synthesizes recent advancements in architectures for representation learning, encompassing graph neural networks, attention-based models, and physics-inspired embeddings, which facilitate the extraction of meaningful features from diverse data modalities such as atomic structures, stoichiometries, and spectroscopic data. By bridging traditional computational methods with machine learning, these representations have accelerated property prediction, inverse design, and materials discovery applications, addressing challenges in high-dimensional spaces and sparse datasets. The scope of this narrative review covers the evolution from basic informatics to sophisticated multimodal integrations, highlighting how data ecosystems and learning frameworks contribute to autonomous discovery pipelines. A systems-level perspective is adopted to integrate cross-study insights, revealing synergies between representation learning and closed-loop systems that couple simulations with experiments. Looking ahead, the review posits that continued refinement of these architectures will drive scalable, AI-guided materials engineering, fostering innovations in energy, electronics, and structural materials while emphasizing the need for robust, interpretable models in real-world applications.

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.

In the evolving landscape of computational and data-driven materials engineering, the integration of machine learning and high-throughput methodologies has transformed traditional materials discovery into sophisticated algorithmic processes. This shift emphasizes the need to reframe materials selection algorithms as discovery recommendation systems, where predictive models serve not merely as classifiers but as dynamic recommenders guiding exploration across vast chemical spaces. A conceptual gap persists in how these systems handle the interplay between representation learning, uncertainty quantification, and closed-loop feedback, often leading to suboptimal navigation of multimodal datasets. To address this, we introduce the Adaptive Discovery Recommendation Architecture (ADRA), a novel framework that conceptualizes materials selection as a recommendation engine optimized for epistemic steering in inverse design workflows. ADRA incorporates layered computational logics that balance representation fidelity with inference adaptability, enabling seamless coupling of simulation and experimental data streams. By reframing algorithms through recommendation paradigms, ADRA highlights infrastructure trade-offs in scalability and interpretability, fostering more robust discovery pipelines. Implications extend to materials informatics ecosystems, enhancing autonomous systems in high-throughput computation and foundation models for science. This conceptual reframing underscores the potential for recommendation-based steering to mitigate epistemic risks, ultimately advancing data-driven innovation in materials engineering.