Search

The integration of machine learning into materials discovery has accelerated exploratory processes, yet it often privileges predictive accuracy over interpretive clarity. This manuscript examines the conceptual tensions arising from black-box approaches in materials science, where algorithmic opacity obscures the underlying logics of material behaviors and interactions. By synthesizing recent literature on explainable artificial intelligence within computational materials contexts, the analysis highlights epistemic trade-offs between rapid exploration and the need for explanatory depth. Conceptual interpretations reveal how opaque models may reinforce feedback loops of uncertainty, limiting the integrative understanding of material systems. The framework interprets these dynamics through steering logics that balance algorithmic efficiency with interpretive accessibility, emphasizing ethical considerations in knowledge production. Systems-level insights underscore the interplay between data-driven discovery and human-centric reasoning, suggesting that unexamined opacity could constrain the broader interpretive landscape of materials innovation. This critique advocates for a reflective integration of explainability, not as a corrective add-on, but as an intrinsic dimension of exploratory practices. Ultimately, the discussion fosters a nuanced appreciation of how explanation shapes the conceptual boundaries of discovery, urging a reevaluation of priorities in computational materials paradigms.

Polymer blend systems occupy a central position in soft matter materials science, where macroscopic properties emerge from complex, multi-scale interactions among molecular architecture, phase morphology, and processing history. Although artificial intelligence (AI) is increasingly used to predict properties of polymer blends, most existing approaches prioritize prediction accuracy over interpretability, limiting their contribution to theoretical understanding and rational materials design. This paper introduces a purely conceptual framework for explainable artificial intelligence (XAI)–enabled structure–property mapping in polymer blend systems, positioning interpretability as a foundational epistemic requirement rather than a post-hoc diagnostic. Given the recent advances in machine learning, polymer informatics, and explainable AI, the framework conceptualizes structure–property maps as interpretable landscapes where predictions, feature attributions, and uncertainty coexist as integrated elements. By explicitly incorporating multi-scale descriptors—from molecular chemistry to mesoscopic morphology—and embedding XAI mechanisms such as feature attribution and counterfactual reasoning, the proposed framework aligns data-based insights with established principles of soft matter physics and thermodynamics. Instead of advanced algorithms or empirical models, this work articulates a theoretical architecture that shows how AI-derived representations can support causal reasoning, trade-off analysis, and epistemic restraint in the design of the polymer blend. Ultimately, the paper provides a framework-level perspective on the application of AI in materials science and defends the structure–property mapping approaches, in which interpretability, physical grounding, and uncertainty awareness are central to scientific meaning and responsible materials innovation.

The integration of artificial intelligence (AI) and machine learning (ML) into materials science has fundamentally transformed how material properties are predicted, analyzed, and understood. While early data-driven approaches emphasized predictive accuracy and high-throughput screening, recent advances are increasingly focusing on interpretability and explainability, enabling AI models to contribute to mechanistic scientific insight rather than functioning as opaque black boxes. This study examines the evolution of interpretable AI in materials science and highlights the transition from property prediction to explanation-driven understanding of structure–property relationships. In this thesis, we investigate the progress in machine learning frameworks that operate with limited or implicit structural information, alongside the growing use of explainable AI (XAI) techniques to uncover physically meaningful descriptors, atomic-scale interactions, and microstructural drivers of material behavior. Methods such as graph-based learning, attention mechanisms, feature attribution, and uncertainty-aware modeling are discussed for their ability to improve model reliability, expose data bias, and guide hypothesis generation. Representative applications across alloys, perovskites, organic semiconductors, and ferroelectric materials demonstrate how interpretable models have revealed governing mechanisms spanning atomic, mesoscopic, and macroscopic length scales. Beyond individual case studies, this study examines persistent challenges in interpretable materials AI, including data quality, generalizability, explanation stability, and computational overhead. We argue that interpretability is not merely an auxiliary feature but a prerequisite for trustworthy and scientifically helpful AI in materials research. By synthesizing recent methodological and application-driven advances, this review positions interpretable AI as a critical enabler of mechanism-oriented discovery, experimental validation, and theory development, ultimately advancing AI from a predictive accelerator to an integral partner in scientific understanding.

In the rapidly evolving intersection of artificial intelligence (AI) and materials science, interpretability techniques promise to bridge computational predictions with scientific understanding. This manuscript proposes a novel conceptual framework that reconceptualizes interpretability as a process of scientific translation, wherein AI outputs are systematically mapped onto material mechanisms. We define AI outputs as encompassing feature attributions, counterfactuals, attention- or saliency-style signals, latent representations/embeddings, surrogate trends, and natural-language rationales. Materials mechanisms, in turn, are formalized as entities and causal relations across atomic/defect chemistry, phase stability/transformations, diffusion/transport, microstructure evolution, and processing–structure–property linkages. The framework addresses the explanation gap by arguing that raw interpretability signals do not inherently constitute mechanistic explanations, particularly in materials science, where multi-scale complexities amplify translation challenges. Through a stepwise translation model, we introduce validity gates—such as scope delimitation, identifiability checks, invariance assessments, causal plausibility evaluations, and scale consistency verifications—to ensure rigorous mapping from AI signals to mechanistic claims. This approach theorizes translation failure modes, including proxy misalignments, confounding interferences, domain shifts, scale mismatches, and narrative overreaches, and delineates strategies to contain them. By synthesizing prior typologies of AI outputs and mechanistic constructs in materials, the framework advances a structured pathway for deriving legitimate scientific insights from AI, fostering theoretical progress in applied AI for materials discovery without empirical validation.

Artificial intelligence (AI) has become increasingly effective at predicting material properties from microstructure-informed representations, enabling rapid screening and accelerated decision-making. Yet, the “explanations” attached to these predictive systems frequently fail to support the kind of understanding required in microstructure–property science—namely, transferable mechanisms, intervention-relevant guidance, and defensible generalization under realistic shifts in processing, measurement, and operating regimes. This conceptual paper argues that explanation failure in materials AI is often structural rather than incidental: many popular explanation toolkits are optimized for interpreting model behavior rather than for producing scientifically legitimate accounts of why a microstructure yields a property outcome. We define the microstructure–property explanation gap as the persistent mismatch between what explainability tools can formally justify and what materials reasoning demands for action. To anatomize this gap, we identify four recurring causes: representational non-identifiability, confounding by processing history, multi-scale emergence, and instability under distribution shift. Building on this anatomy, we propose a novel theoretical framework—the Explanation Integrity Triad (EIT)—which evaluates any AI explanation along three axes: Representational Integrity, Causal Integrity, and Operational Integrity. The EIT provides a domain-specific vocabulary to prevent mechanistic overclaims and align explanation practices with scientific accountability in applied materials informatics.

The rapid integration of artificial intelligence (AI) into materials science has enabled unprecedented predictive capabilities across a wide range of properties and structures. However, the predominantly black-box nature of these models limits their epistemic role, confining them largely to correlative tools rather than instruments capable of supporting genuine scientific reasoning. This conceptual manuscript introduces a novel theoretical framework that delineates a structured pathway for validating AI systems as materials reasoning tools. Drawing on recent advances in explainable and interpretable AI, as well as philosophical accounts of scientific reasoning, the framework articulates a progressive sequence of validation stages: establishing transparency and interpretability, extracting mechanistically meaningful explanations, assessing reasoning fidelity through inferential behavior, and integrating AI systems as instruments within the broader scientific knowledge cycle. The approach is deliberately architecture-agnostic and avoids empirical prescriptions, focusing instead on the conceptual and epistemic conditions required for scientific legitimacy. By explicitly bridging predictive performance with explanatory depth, inferential robustness, and alignment with physical theory, the proposed pathway reframes how success in materials AI is evaluated. It provides a foundation for distinguishing advanced predictive engines from systems capable of contributing to hypothesis generation, theory refinement, and cumulative understanding. In doing so, the framework addresses persistent barriers to the acceptance of AI as a scientific partner in materials research. It offers a principled basis for future methodological and evaluative developments.

The rapid integration of artificial intelligence (AI) into materials science marks a profound shift in how materials are discovered, characterized, and optimized. Rather than functioning merely as a computational aid, AI increasingly operates as an epistemic instrument that reshapes scientific workflows, decision-making practices, and notions of explanation within the field. This narrative review examines the conceptual foundations underpinning applied AI in materials science, with a particular focus on core definitions, implicit and explicit assumptions, and unresolved debates that continue to shape the domain. Key AI paradigms—including supervised, unsupervised, and reinforcement learning—are situated within materials-specific contexts such as property prediction, structure–property mapping, and autonomous experimentation. The review critically interrogates foundational assumptions regarding data quality, representativeness, generalization, and model transferability, highlighting how these assumptions condition both the successes and failures of AI-driven materials research. Persistent debates surrounding interpretability, epistemic trust, ethical responsibility, and environmental sustainability are synthesized from recent literature published. By articulating both the transformative potential and the conceptual limitations of applied AI, this review underscores the necessity of rigorous validation, transparent reasoning, and interdisciplinary collaboration to ensure that AI contributes robustly and responsibly to materials innovation.

The integration of artificial intelligence (AI) and machine learning (ML) into materials science has revolutionized the discovery, design, and optimization of new materials, enabling accelerated predictions of properties and behaviors previously unattainable with traditional methods. However, the “black-box” nature of many advanced AI models poses significant challenges, including a lack of transparency that hinders scientific understanding, trust, and practical adoption in materials research. This narrative review explores the concept of interpretability in materials AI, focusing on what constitutes an “explanation” and how it should be conceptually evaluated. Drawing from recent advancements in explainable AI (XAI), we delineate definitions of explanations tailored to materials informatics, emphasizing their role in bridging computational predictions with physical insights. We examine thematic aspects such as intrinsic versus post-hoc interpretability methods, the multidimensional nature of explanations (e.g., local vs. global, feature-based vs. mechanistic), and conceptual frameworks for evaluation, including criteria like fidelity, comprehensibility, robustness, and domain-specific relevance. By synthesizing the literature, we highlight how explanations can enhance materials discovery across alloy design, catalyst development, and polymer engineering, while addressing gaps in current evaluation practices. The review underscores the need for standardized conceptual metrics that go beyond quantitative benchmarks to incorporate qualitative, human-centered assessments in materials science contexts. Ultimately, this work aims to guide researchers toward developing interpretable AI systems that not only predict but also elucidate underlying material phenomena, fostering a more insightful and ethical application of AI in materials innovation.

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.

This review article examines the literature on scientific explanation in AI-driven materials science, focusing on the conceptual foundations and evaluative criteria that distinguish genuine scientific explanation from the predictive and interpretive outputs commonly produced by machine learning models in the field. The methodology involved a systematic search across major databases and targeted journals using predefined strings related to scientific explanation, explainable AI (XAI), and interpretability in materials contexts, resulting in the inclusion of 30 peer-reviewed publications from 2017 to 2024 that directly address the intersection of philosophical theories of explanation and practical AI applications in materials discovery and property prediction. Philosophical theories of explanation, including the deductive-nomological model of Hempel and Oppenheim, the causal-mechanical account advanced by Salmon, unificationist approaches that emphasize the integration of disparate phenomena, and pragmatic frameworks that treat explanations as context-dependent answers to why-questions, provide essential benchmarks against which current materials AI practices can be assessed. In current materials AI literature, explanation is frequently conflated with prediction or post-hoc interpretability techniques such as feature importance scores and attention visualizations, as seen in comprehensive surveys of machine learning for molecular and materials science and recent advances in solid-state applications. Yet, these approaches often remain correlational rather than mechanistically grounded. XAI methods applied to materials problems, including SHAP-based feature attribution, attention mechanisms in graph neural networks, surrogate modeling, and counterfactual generation, offer valuable local insights but fall short of meeting the standards of scientific explanation due to their inherent limitations in capturing causality, multi-scale mechanisms, and physical plausibility. Ultimately, this review articulates adapted criteria for scientific explanation tailored to materials science’s multi-scale and emergent challenges and proposes actionable recommendations to bridge the gap between XAI outputs and robust explanatory accounts, urging the community to prioritize mechanistic understanding over mere predictive accuracy to advance trustworthy and insightful AI-driven discovery.