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Generative models have emerged as pivotal tools in materials science, promising to accelerate the discovery of novel compounds by synthesizing structures with desired properties. However, this paper contends that such models often perpetuate an illusion of novelty, in which outputs appear innovative but are constrained by inherent biases in training data, algorithmic architectures, and evaluation paradigms. Drawing on a synthesis of recent literature, we examine how generative approaches, including variational autoencoders, generative adversarial networks, and diffusion models, inadvertently replicate existing material patterns rather than generating truly unprecedented designs. This illusion arises from data imbalances favoring well-studied systems like oxides, overfitting to historical datasets, and a lack of mechanisms to enforce epistemic diversity. We propose a novel conceptual framework that disentangles apparent from substantive novelty through a tripartite lens: data provenance, model interpretability, and output validation against scientific values such as generalizability and explanatory power. By applying this framework, researchers can mitigate illusory outcomes and foster authentic advancements in materials informatics. The analysis underscores the need to integrate philosophical insights into scientific values to refine generative paradigms, ultimately enhancing the reliability of AI-driven materials discovery. This conceptual exploration highlights pathways toward more robust, value-aligned generative systems, without prescribing empirical validations or simulations.

Materials informatics, the application of data science and machine learning to materials research, has revolutionized the discovery and design of new materials. However, the field faces significant challenges in reporting failure cases, negative results, and biases, which are often silenced in the literature. This review examines documented failures in materials informatics, such as data bias, model overoptimism, and reproducibility issues, and highlights the systemic factors that lead to their underreporting. Drawing on 30 recent peer-reviewed articles, we explore themes including data quality, algorithmic limitations, and publication bias. The objectives are to assess what is typically documented, identify silenced aspects, such as unsuccessful experiments, and propose strategies for more transparent reporting. By addressing these gaps, the review aims to foster a more robust and trustworthy materials informatics ecosystem, ultimately accelerating sustainable innovation in materials science.

Materials informatics has become a central paradigm in materials science, leveraging machine learning and large-scale datasets to accelerate property prediction, discovery, and design. However, prevailing approaches often treat data as a neutral substrate for modeling, obscuring the value-laden processes through which data is generated. Measurement choices—what properties to quantify, which materials to prioritize, and which experimental or computational protocols to employ—are inherently shaped by epistemic commitments, practical constraints, and broader societal priorities. These choices embed values into data infrastructures, systematically influencing which material phenomena become visible and which remain obscured in downstream models. This manuscript advances a conceptual framework that interprets measurement choices as value-mediated interfaces linking scientific priorities to data constitution and modeling feedback in materials informatics. The framework elucidates how value horizons, choice architectures, data formation processes, and modeling circuits interact to produce steering logics, trade-offs, and path-dependent dynamics. By reframing data bias as a constitutive outcome of value-conditioned measurement rather than a purely technical artifact, the framework reveals characteristic failure modes—including value lock-in, patterned absences, and self-reinforcing feedback—that constrain epistemic exploration. Integrating insights from materials informatics, data bias studies, and philosophical analyses of scientific practice, the framework provides a diagnostic lens for understanding the non-neutrality of data in iterative AI-driven workflows. Rather than prescribing methodological interventions, it foregrounds the epistemic consequences of measurement decisions, inviting greater reflexivity in shaping data landscapes over time. This perspective repositions materials informatics as an evolving epistemic system whose possibilities and limits are co-produced by values, measurements, and models.

The integration of artificial intelligence (AI) into materials science has substantially accelerated property prediction and materials screening. Yet, the predominance of data-driven correlations has exposed a persistent epistemic gap between predictive success and the derivation of interpretable, generalizable design rules. This conceptual manuscript develops a theoretical framework for knowledge extraction in materials AI that explicitly addresses this gap by reframing the transition from correlations to design rules as a staged epistemic process rather than a by-product of model performance. Drawing on literature in materials informatics, data bias, and philosophy of science, the framework organizes knowledge extraction into four interconnected stages—Correlation Mapping, Bias Interrogation, Value Integration, and Rule Synthesis—linked through continuous epistemic validation. The model foregrounds epistemic agency, requiring explicit scrutiny of assumptions, biases, and value commitments before causal inference. Six propositions articulate the conditions under which AI-derived correlations may legitimately support prescriptive design claims, emphasizing reflexive feedback and epistemic governance. By conceptualizing knowledge extraction as a norm-governed process of justification, this work provides a theoretical scaffold for transforming AI outputs into scientifically defensible design rules, contributing to a more reliable and responsible epistemology of materials discovery.

The integration of artificial intelligence (AI) into materials science has ushered in an era of semi-autonomous systems that accelerate discovery through predictive modeling, high-throughput screening, and adaptive experimentation. These systems offer substantial promise for addressing global challenges in energy, sustainability, and advanced manufacturing; however, their reliance on data-driven inference introduces risks related to bias propagation, epistemic uncertainty, and misalignment with scientific values. Conventional approaches treat human oversight primarily as an external corrective mechanism—post hoc monitoring or intervention in response to model outputs. This paper proposes a conceptual reframing wherein human oversight is repositioned as an intrinsic element of system design. Rather than viewing control as supervision layered atop an autonomous core, oversight is conceptualized as deliberate architectural choices that embed human judgment into the foundational structure of semi-autonomous materials AI. Drawing on literature from materials informatics, data bias mitigation, explainable AI, and human-AI collaboration, the proposed framework delineates three interdependent dimensions: epistemic boundary-setting, value-aligned modulation, and adaptive reflexivity. This reframing shifts the discourse from mitigating human absence to engineering human presence, fostering systems that are inherently more robust, interpretable, and aligned with the normative goals of scientific inquiry. By reconceptualizing oversight as design, the framework offers a pathway to responsible integration of AI in materials discovery without presupposing full autonomy or diminishing human agency.

The integration of artificial intelligence into materials science has accelerated property prediction, inverse design, and discovery pipelines. Yet, the reliability of resulting scientific claims remains vulnerable to distribution shifts—systematic differences between training and inference data distributions arising from variations in synthesis protocols, characterization instruments, environmental conditions, or sampling biases. This purely conceptual manuscript develops a novel theoretical framework for robust materials AI inference in the presence of such shifts. We posit that distribution shifts do not merely degrade predictive accuracy but fundamentally alter the epistemic status of scientific claims by introducing unaccounted covariances between material descriptors and latent generative processes. The framework reconceptualizes inference as a multi-layered epistemic process: (i) shift ontology delineation, (ii) value-laden alignment of data representations with domain invariants, and (iii) claim robustness via counterfactual stabilization. By synthesizing insights from materials informatics, machine learning theory on distribution shifts, and philosophical analyses of epistemic values in science, we argue that robust inference requires explicit modeling of shift-induced epistemic uncertainty rather than mitigation as a post hoc engineering concern. This theory provides a conceptual scaffold for evaluating the validity of AI-derived materials claims across heterogeneous datasets, advancing a shift from performance-centric to epistemically grounded AI deployment in materials science.

The integration of physical principles into machine learning (ML) frameworks has emerged as a transformative approach in materials science, addressing the limitations of purely data-driven models by incorporating domain knowledge to enhance predictive accuracy, generalizability, and interpretability. This narrative review explores the conceptual taxonomies of physics-integrated ML methods, their applications in materials discovery and design, and the associated challenges in data bias and ethical considerations. Drawing on recent peer-reviewed literature, we classify physics-integration strategies such as physics-informed neural networks (PINNs), hybrid models combining ML with physical simulations, and constraint-based learning, and highlight their roles in solving complex problems such as material property prediction, microstructure analysis, and phase stability. We also examine how data biases in training datasets can propagate errors and inequities in model outputs, and discuss the ethical values underpinning the use of AI in scientific research, including transparency, accountability, and societal impact. The review underscores the potential of these methods to accelerate innovation in materials science while emphasizing the need for rigorous validation and interdisciplinary collaboration. By synthesizing current advancements, this article aims to provide a foundational understanding for researchers and practitioners, paving the way for future developments in this interdisciplinary field.

Materials artificial intelligence (MAI) has revolutionized the discovery, design, and optimization of new materials by leveraging machine learning algorithms to analyze complex datasets and predict properties with high accuracy. However, the rapid proliferation of MAI tools has raised critical questions about benchmarking practices, which are essential for evaluating model performance, ensuring reproducibility, and addressing ethical concerns. This narrative review examines current benchmarking frameworks in MAI, highlighting what is effectively measured—such as predictive accuracy and computational efficiency—and what is often overlooked —such as data bias, interpretability, fairness, and ethical implications. Drawing on recent advances in frameworks such as JARVIS-Leaderboard and Matbench, the review discusses challenges in data quality, reproducibility, and the integration of explainable AI (XAI) methods. It also explores active learning strategies for optimizing materials discovery under limited data conditions and proposes directions for more inclusive and transparent benchmarking. By synthesizing insights from diverse studies, this review aims to guide future MAI research toward robust, equitable, and ethically sound practices that accelerate innovation while mitigating risks.