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The integration of artificial intelligence (AI) and machine learning (ML) into materials science, often referred to as materials informatics or materials AI, has accelerated the discovery, design, and optimization of advanced materials. However, materials science frequently operates in small-data and sparse-regime conditions, where datasets are limited in size (often tens to hundreds of samples), high-dimensional, imbalanced, or sparsely populated due to the high cost, time, and complexity of experimental measurements and high-fidelity simulations. This narrative review synthesizes recent advances in methods tailored to these constraints, categorizing approaches at the data-source level (e.g., literature extraction, database construction, high-throughput workflows), algorithmic level (e.g., support vector machines, Gaussian process regression, ensemble models, imbalanced learning techniques), and strategic level (e.g., active learning, transfer learning). Key assumptions underlying these methods are examined, including similarity between source and target domains for transfer learning, representativeness of initial samples and reliable uncertainty quantification in active learning, and the validity of physical priors or inductive biases in physics-informed approaches. The review also addresses inherent limits, such as risks of overfitting, poor generalization beyond the training distribution, sensitivity to data quality and noise, challenges in uncertainty calibration, and dependence on domain expertise. By highlighting successful applications in property prediction, alloy design, and perovskite optimization, this work elucidates the current capabilities and boundaries of small-data and sparse-regime learning in materials AI, guiding researchers navigating data-limited environments.

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.

Autonomous and semi-autonomous laboratories represent a transformative paradigm in materials science, integrating artificial intelligence, robotics, and high-throughput experimentation to accelerate discovery and optimization processes. This review examines the conceptual foundations of these systems, including closed-loop optimization, machine learning algorithms, and modular hardware architectures. We explore their applications in areas such as alloy development, perovskite synthesis, and nanoparticle engineering, highlighting successes that have reduced discovery timelines from years to days. However, we also critically assess associated risks, including data quality issues, algorithmic biases, ethical concerns in resource allocation, and potential safety hazards from unsupervised operations. Drawing on recent advances, we propose balanced implementation strategies that maximize innovation while mitigating risks. The review underscores the need for interdisciplinary collaboration to realize the full potential of these technologies in addressing global materials challenges.

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.

Generative models in materials science have emerged as powerful tools for proposing novel atomic structures, compositions, and functional properties. Yet, their scientific evaluation remains conceptually underdeveloped and fragmented across statistical proxies that rarely capture the true relevance to materials. This review systematically examines the conceptual foundations of scientific evaluation for generative materials AI by targeting 30 peer-reviewed publications spanning 2017–2026 and employing a PRISMA-guided methodology focused on evaluation metrics, physical plausibility, chemical validity, synthesizability, novelty, and utility. The evaluation dimensions extend far beyond conventional statistical metrics such as validity percentages or reconstruction error to encompass six interlocking scientific criteria—chemical validity, structural plausibility, property accuracy, synthesizability, novelty, and utility—that together define whether a generated material constitutes a genuine scientific artifact rather than a computational curiosity. Current evaluation practices, as documented across the literature, remain heavily anchored in validity scores, uniqueness counts, and nearest-neighbor novelty checks, with approximately 68% of studies relying primarily on chemical-validity filters and only 22% incorporating any form of synthesizability assessment, revealing a persistent gap between computational convenience and experimental realism. Critical analysis reveals that these practices are necessary yet profoundly insufficient, frequently conflating statistical fidelity with scientific value and overlooking failure modes such as physically unstable geometries or literature-overlooked duplicates. Emerging frameworks, including multi-objective physics-informed scoring, retrospective validation against subsequent experimental discoveries, and downstream task benchmarking, offer promising pathways toward more rigorous standards. Yet significant gaps persist in the absence of community-wide benchmarks, reliable predictors of synthesizability, and domain-specific utility metrics. This review, therefore, offers actionable recommendations for authors, reviewers, and the broader community to elevate generative materials AI from pattern generation to verifiable scientific discovery, ensuring that evaluation protocols align with the epistemological demands of materials science itself.

Artificial intelligence is rapidly moving beyond its early role as a pattern-recognition and predictive-modelling tool in materials science. What began as an acceleration strategy for screening known datasets is now becoming a broader transformation of how materials hypotheses are generated, tested, and refined. The central problem is that this transformation is often described in fragments: predictive models in one literature, generative design in another, physics-informed learning in another, and autonomous laboratories in yet another. A unified conceptual synthesis is needed to explain how these streams collectively move AI from passive assistant to active scientific collaborator. This integrative review traces the evolution of AI in materials science from 2017 to 2026. It frames the field through the idea of the AI co-scientist: an intelligent system that can recognise patterns, propose candidates, incorporate physical constraints, select experiments, and learn from feedback. The review integrates 31 peer-reviewed articles spanning materials informatics, machine learning, generative AI, inverse design, physics-informed modelling, active learning, autonomous experimentation, and self-driving laboratories. It does not present new empirical data, meta-analysis, or bibliometric mapping. The synthesis identifies four major evolutionary stages: pattern recognition, generative design, physics-integrated AI, and autonomous experimentation. These stages are not isolated phases but mutually reinforcing capabilities that increasingly connect computation, synthesis, characterisation, and human judgement. The review concludes that AI is becoming a genuine partner in materials discovery, but this transition depends on trustworthy data infrastructure, interpretable models, robust experimental integration, and new norms for human–AI collaboration. The co-scientist paradigm offers a forward-looking framework for understanding how materials science may be reorganised around closed-loop intelligence.

This review examines the literature on ethical frameworks for artificial intelligence (AI) applied to materials science and discovery, synthesizing insights from 31 peer-reviewed publications spanning 2017 to 2026 to trace the evolution from high-level principles to practical implementation. The methodology involved a systematic search across Web of Science, Scopus, arXiv, and PhilPapers using targeted strings such as “ethics AI materials science,” “responsible AI materials discovery,” “ethical framework AI science,” “dual use materials AI,” “AI ethics principles materials,” “governance AI materials research,” “justice AI materials discovery,” and “value alignment materials AI,” with inclusion limited to peer-reviewed works directly addressing ethical dimensions in scientific or materials contexts, yielding 31 core references after PRISMA-style screening of over 500 initial results. Major ethical principles for AI—beneficence, non-maleficence, autonomy, justice, explicability, and sustainability—are surveyed as foundational guides originally developed in broader AI ethics literature but rarely adapted to materials-specific applications. The current state of materials AI literature reveals a predominant focus on technical acceleration of discovery, with explicit ethical engagement appearing in fewer than 20% of surveyed works and often limited to passing mentions rather than systematic analysis. Materials-specific ethical challenges, including dual-use risks in weaponizable materials, environmental harms from resource-intensive AI-driven synthesis, equity gaps in global access to discoveries, labor displacement through automation, intellectual property ambiguities, and intergenerational justice concerns, remain largely unaddressed despite the field’s rapid growth. Significant gaps persist in operationalizing principles, developing governance mechanisms, and providing domain-tailored guidance, underscoring an urgent need for actionable recommendations to bridge the principles-practices divide and foster responsible materials AI innovation that prioritizes societal benefit, sustainability, and justice.

This review examines the problem of scientific consensus formation in AI-driven materials science by systematically analyzing conceptual approaches from philosophy and sociology of science alongside empirical developments in computational materials research, drawing exclusively on 31 peer-reviewed publications from 2017–2026 identified through targeted searches in Web of Science, Scopus, arXiv, and PhilPapers using terms such as “scientific consensus” AI materials, “consensus formation” machine learning science, “disagreement” materials AI, “benchmark” consensus materials informatics, “epistemic consensus” AI science, “paradigm” materials AI, “scientific disagreement” computational science, and “consensus mechanism” AI research, with inclusion criteria limited to papers addressing epistemology, disagreement, uncertainty, benchmarks, or paradigm dynamics in data-driven disciplines and exclusion of purely technical performance reports. Consensus concepts are traced from logical-positivist agreement on theories through Kuhnian paradigms and Mertonian social processes to Bayesian convergence and pragmatic problem-solving necessities, revealing how each framework illuminates different facets of knowledge coordination in materials science. AI’s impact on consensus formation operates through six distinct mechanisms—accelerated hypothesis validation, model disagreement, benchmark-driven focal points, opacity-induced dissent, data-driven convergence, and authority shifts—both facilitating rapid agreement on material properties and simultaneously generating new forms of epistemic fragmentation. These dynamics create profound tensions and paradoxes, including the trade-off between speed and deliberation, convergence versus diversity, predictive agreement versus explanatory understanding, local versus global consensus, and human versus AI authority, while exposing critical gaps such as the absence of a dedicated theory for AI-mediated consensus, the scarcity of empirical studies tracking real-time consensus processes in materials AI communities, and unresolved questions about managing productive disagreement. Recommendations are offered for researchers, journals, and the broader community to distinguish model agreement from scientific consensus, institutionalize empirical consensus studies, preserve productive dissent, and develop governance protocols that harness AI’s epistemic power without sacrificing critical scrutiny, thereby guiding the field toward more reflexive and robust knowledge production in the age of AI-augmented materials discovery.