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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.

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.

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.