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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 multi-model and hybrid artificial intelligence (AI) systems has revolutionized materials research by enabling the efficient analysis of complex datasets, the prediction of material properties, and the optimization of design processes. This narrative review examines the architectures of these systems, including ensemble methods, multimodal data fusion, and physics-informed neural networks. It evaluates their applications in areas such as alloy design, nanomaterial synthesis, and battery management. Key trade-offs are discussed, encompassing computational efficiency versus predictive accuracy, data scarcity versus model generalizability, and interpretability versus performance in black-box models. Drawing on recent peer-reviewed literature, the review highlights how these AI approaches accelerate materials discovery while addressing challenges such as uncertainty quantification and scalability. By synthesizing current advancements, this work underscores the potential of hybrid AI to drive sustainable innovation in materials science, with implications for future interdisciplinary research.

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.

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.

The integration of artificial intelligence (AI) into materials science has revolutionized how we predict, design, and discover new materials. Among various AI techniques, ensemble methods have emerged as powerful tools that leverage the collective intelligence of multiple models to enhance prediction accuracy and reliability. This review explores the application of ensemble methods in materials AI, focusing on why individual models disagree and the scientific implications of such disagreements. By analyzing recent advancements, we highlight how ensemble approaches address uncertainties in material property prediction, phase stability, and electronic structure calculations. The review synthesizes insights from peer-reviewed literature published, emphasizing the role of ensemble methods in providing robust predictions and uncovering underlying physical principles. Ultimately, understanding model disagreement not only improves computational efficiency but also deepens our scientific understanding of material behavior.

Artificial intelligence is rapidly reshaping materials science by accelerating property prediction, synthesis planning, and materials design. Yet most AI models for materials are developed and validated under implicit stationary assumptions, while real deployments unfold in time-varying environments where materials, sensors, and processes evolve. This review synthesizes what is currently known about temporal generalization in materials AI—the capacity of models to remain reliable as data distributions and underlying mechanisms change. We distinguish two dominant degradation pathways: drift, in which input statistics or input–output relationships shift over time, and model aging, in which learned representations become obsolete as systems evolve. Drawing on evidence across biosensing and wearables, electrochemical energy storage, polymer synthesis, automated laboratories, and industrial manufacturing, we summarize how temporal failures arise, how they are detected, and why they often remain silent until performance drops become consequential. We then evaluate mitigation strategies—including domain adaptation, incremental and continual learning, active data acquisition, uncertainty-aware prediction, and human–AI feedback loops—highlighting where they succeed, where they break down, and the constraints that limit their scalability in real-world settings. Finally, we identify key gaps: limited longitudinal datasets, weak standardization of temporal evaluation protocols, underexplored multimodal temporal fusion, and insufficient emphasis on prevention rather than detection. We conclude with a forward agenda for resilient materials AI built around lifecycle monitoring, benchmarkable temporal stress tests, and hybrid frameworks that integrate mechanistic knowledge with adaptive learning to sustain reliability over time.

Machine learning (ML) has become a central driver of modern materials discovery, fundamentally reshaping how materials are designed, screened, and experimentally realized. This review examines recent advances in ML-accelerated materials discovery and emphasizes the ongoing progress in material representation and descriptor development toward fully autonomous experimental platforms. We discuss how increasingly sophisticated descriptors—ranging from composition-based features and structure-aware representations to ab initio–derived and learned embeddings—have improved predictive accuracy, data efficiency, and physical interpretability across diverse materials systems. Based on these findings, we discuss the evolution of ML frameworks for property prediction, classification, and inverse design, with particular attention to uncertainty-aware modeling, multiobjective optimization, and explainable learning strategies that bridge predictive performance with scientific insight. The study also highlights the growing role of active learning and generative models in efficiently navigating vast chemical and structural spaces, enabling data-efficient exploration and hypothesis-driven discovery. At the frontier of these developments, autonomous experimental systems integrate ML with robotics to form closed-loop workflows that iteratively design, execute, and refine experiments with minimal human intervention. Applications spanning perovskites, alloys, energy materials, and nanostructures illustrate the broad impact of these approaches in overcoming traditional trial-and-error limitations. Finally, we discuss persistent challenges associated with data scarcity, extrapolation, interpretability, and system integration, and outline future directions toward more robust, scalable, and sustainable autonomous materials discovery. Collectively, these advances represent a paradigm shift from passive data-driven prediction to intelligent, self-guided 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.

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 systematically maps the scientific blind spots in the materials artificial intelligence literature by conducting a targeted search across key databases and journals to identify both what is heavily studied and what remains systematically invisible. The analysis organizes these blind spots into five interconnected categories—data, methods, evaluation, epistemic, and social—drawing on core peer-reviewed publications that collectively document the selective lens through which the field presents its progress. Key findings reveal that data blind spots center on underrepresented chemistries, structures, and operational conditions that leave critical real-world properties unmodeled; methodological blind spots arise from the dominance of correlation-driven approaches while uncertainty quantification, small-data techniques, and causal methods receive scant attention; evaluation blind spots manifest in the near-total absence of distribution-shift testing, robustness checks, and negative-result reporting that inflate perceived reliability; epistemic blind spots persist through prediction-without-explanation paradigms and the failure to articulate model boundary conditions or failure modes; and social blind spots ignore value-laden assumptions, equity considerations, and the broader societal and environmental implications of materials AI deployment. Synthesis across categories uncovers systemic patterns such as positive publication bias creating self-reinforcing feedback loops, methodological innovation consistently outpacing rigorous evaluation, data gaps mirroring decades-old research priorities, and epistemic shortcomings that propagate through every layer of the pipeline. Recommendations, therefore, target authors, reviewers, journals, and funders with concrete actions to surface these blind spots, thereby enabling a more balanced, reproducible, and societally relevant materials AI research agenda that closes the gap between published claims and real-world impact.

This review systematically examines the handling—or more often the neglect—of domain shift within the materials machine learning literature published between 2017 and 2023, drawing on a targeted search of peer-reviewed publications across specialized databases and journals to compile and analyze exactly 30 representative studies that span foundational overviews, application-focused works, and methodological explorations. Domain shift in materials science takes four distinct yet interrelated forms—temporal, compositional, experimental, and theoretical—each arising from the inherently heterogeneous nature of materials data sources that range from evolving laboratory protocols and diverse chemical families to inter-laboratory variations and discrepancies between computational approximations and experimental realities. Current practices reveal that explicit acknowledgment of domain shift remains rare, with the majority of papers proceeding under the default assumption of identical training and test distributions. At the same time, detection methods and adaptation strategies appear in fewer than one in five studies, leaving models vulnerable to silent degradation when deployed on real-world materials problems. The surveyed methods for handling domain shift include statistical detection techniques, domain-adversarial training frameworks, feature-alignment approaches, and shift-robust evaluation protocols, many of which have been proposed in adjacent machine-learning fields yet remain underutilized in materials contexts despite their direct relevance to property prediction and inverse design tasks. Collectively, these findings underscore the urgent need for standardized shift-reporting protocols, the development of materials-specific out-of-distribution benchmarks, and the integration of domain-adaptation pipelines into routine workflows, thereby elevating the reliability, generalizability, and practical utility of machine-learning models in accelerating materials discovery.

Multi-fidelity modeling has become an indispensable paradigm in artificial intelligence for materials science, offering a structured way to integrate data from simulations of varying computational expense and accuracy to accelerate the discovery and optimization of novel materials while mitigating the prohibitive costs associated with high-fidelity methods alone. This review systematically examines the conceptual foundations, underlying assumptions, and inherent trade-offs of multi-fidelity approaches through a targeted analysis of 30 peer-reviewed publications published between 2017 and 2023, identified via a rigorous literature search across databases such as Web of Science and Scopus that employed the exact search strings specified in the reference discovery protocol. The conceptual foundations rest on the hierarchical organization of fidelity levels, wherein low-fidelity models deliver rapid, broad-coverage approximations that serve as scaffolds for correction and refinement by higher-fidelity calculations through surrogate-based information transfer, thereby enabling efficient navigation of high-dimensional material design spaces. Key assumptions—such as the presence of meaningful correlation and smoothness between fidelity outputs, as well as linearity in the mapping between them—are scrutinized alongside the trade-offs they impose between computational cost, predictive accuracy, generalization capacity, and uncertainty handling. Methods ranging from Gaussian process co-kriging to neural network transfer learning are conceptually surveyed for their role in bridging fidelity gaps. At the same time, materials-specific applications in alloys, polymers, and interfaces illustrate both demonstrated successes and context-dependent limitations. Significant gaps persist in the literature, notably the infrequent validation of core assumptions and the absence of standardized benchmarks for multi-fidelity tasks, prompting recommendations for explicit assumption testing, quantitative trade-off reporting, and community-driven development of open benchmarks and reporting standards to elevate the rigor of multi-fidelity materials AI. Through this structured examination, the review underscores that while multi-fidelity frameworks hold transformative potential, their conceptual maturity requires sustained critical attention to assumptions and trade-offs if they are to support next-generation materials innovation reliably.

This review systematically examines the conceptual treatment of causality within materials informatics literature published between 2017 and 2024, drawing exclusively on a curated set of 26 studies identified through targeted and broadened searches across Web of Science, Scopus, arXiv, and specialized databases using terms such as “causal inference,” “causality materials informatics,” “structural causal model,” “directed acyclic graph,” “intervention materials design,” and “counterfactual materials prediction,” with inclusion criteria focused on relevance to materials AI while allowing broader engineering and general causal frameworks where they intersect with materials problems. The analysis reveals a pronounced dominance of correlation-based approaches in materials artificial intelligence, where predictive models achieve impressive statistical fits for structure-property relationships yet seldom progress to robust causal claims, as evidenced by the majority of surveyed works prioritizing accuracy metrics over interventional or counterfactual reasoning. Key causal concepts and frameworks, primarily drawn from Pearl’s foundational hierarchy of association, intervention, and counterfactuals as well as structural causal models and directed acyclic graphs, are introduced and contrasted with their limited adoption in the field. Causal methods that have been applied, albeit sparingly, to materials informatics—ranging from data-driven causal discovery to Bayesian causal modeling—are surveyed alongside their strengths and context-specific limitations. Persistent challenges, including the rarity of randomized interventions in experimental materials workflows and the confounding effects inherent in high-dimensional observational datasets, are highlighted as barriers that leave substantial gaps in the literature. Ultimately, this review offers targeted recommendations for authors, reviewers, and the broader community to integrate causal reasoning more explicitly, thereby moving materials informatics from correlational prediction toward actionable intervention and counterfactual understanding essential for autonomous materials design.

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.

The rapid integration of artificial intelligence (AI) into materials science has transformed workflows for property prediction, inverse design, and autonomous experimentation. Yet, it has simultaneously introduced profound challenges regarding when and how human researchers should trust AI-generated recommendations in high-stakes contexts. This review systematically examines conceptual models of trust in materials AI by synthesizing interdisciplinary insights from human factors engineering, psychology, and human-computer interaction with domain-specific literature on automated materials discovery. A targeted literature search across Web of Science, Scopus, arXiv, and the ACM Digital Library, employing search strings focused on trust in AI systems, trust calibration, trustworthiness, and human-AI interaction in scientific discovery, yielded approximately 450 initial records. After applying inclusion criteria limited to peer-reviewed English-language publications from 2017 to 2024 that addressed conceptual foundations, frameworks, or applications of trust in AI (with explicit relevance to scientific or materials contexts), 30 studies were selected for in-depth analysis following a PRISMA-style screening process. Conceptual foundations of trust are reviewed, drawing on foundational definitions that position trust as an attitude that an agent will help achieve goals under conditions of uncertainty and vulnerability, while distinguishing it from mere reliance and emphasizing the necessity of calibration for appropriate reliance levels. Existing frameworks for trust in AI are surveyed, revealing recurring components such as competence, integrity, benevolence, performance, process, and purpose, each evaluated for strengths and limitations when transposed to materials AI environments characterized by black-box models, rare events, and high economic or safety stakes. The current state of trust research in materials AI demonstrates a pronounced gap: the majority of studies prioritize predictive accuracy and scalability, with only emergent attention to trustworthiness, explainability, or human trust dynamics. Dimensions of trust tailored to materials AI—predictive competence, uncertainty calibration, transparency, robustness, benevolence, and accountability—are proposed and analyzed in relation to domain-specific challenges. This review articulates open questions surrounding trust establishment, post-failure dynamics, and stakeholder variations while offering recommendations for trust-aware design, evaluation, and reporting. By bridging broader AI trust literature with materials science realities, the work advocates for a paradigm shift from accuracy-centric evaluation toward integrated trust models that ensure safe, effective, and ethically sound human-AI collaboration in materials discovery and innovation.

The field of computational and data-driven materials engineering has undergone rapid evolution, driven by advancements in high-throughput computational screening, machine learning algorithms, and integrated workflows that accelerate materials discovery. This review synthesizes recent developments in materials informatics, focusing on platforms that enable efficient exploration of vast chemical spaces through automated computations and data analytics. Key areas include the application of graph neural networks and representation learning for property prediction, active learning strategies to optimize experimental feedback loops, and the integration of multimodal datasets for enhanced model accuracy. High-throughput methods have facilitated discoveries in diverse domains, such as superconductors, battery materials, and high-entropy alloys, by combining density functional theory simulations with machine learning surrogates. Autonomous laboratories and closed-loop systems represent a paradigm shift, allowing self-driving experiments that minimize human intervention while maximizing discovery efficiency. Uncertainty quantification plays a critical role in guiding these processes, ensuring reliable predictions amid sparse data. This narrative review structures the landscape into computational ecosystems, workflow integrations, and discovery outcomes, highlighting cross-study synergies. It positions the field at the cusp of scalable, inverse design paradigms, where data-driven insights bridge simulation and experimentation to address grand challenges in materials science.

The field of computational and data-driven materials engineering has transformed from traditional high-throughput simulations to sophisticated ecosystems integrating machine learning with multimodal datasets for accelerated discovery. This review synthesizes recent advancements in materials informatics, emphasizing the role of graph neural networks and deep learning in processing complex structural and property data. We examine multimodal datasets that combine experimental, computational, and textual modalities, enabling robust representation learning and uncertainty quantification. Integration frameworks are discussed, including active learning loops and multi-fidelity models that bridge simulation and experiment, addressing challenges like data sparsity and distribution shifts. The discovery potential is highlighted through applications in property prediction, inverse design, and autonomous systems, such as identifying stable alloys and energy materials. By providing an original synthesis of these elements, this article underscores the shift toward closed-loop workflows that enhance generalizability and interpretability, while identifying gaps in handling finite-temperature stability and disordered systems. Ultimately, these approaches promise to expand the known materials space by orders of magnitude, fostering innovations in sustainable technologies.

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.