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The integration of artificial intelligence (AI) and machine learning (ML) in materials science has accelerated discovery and design processes, yet it introduces challenges related to failure, uncertainty, and risk. This narrative review examines how the materials AI literature addresses negative outcomes, including model uncertainties, predictive failures, and associated risks in application. Drawing on peer-reviewed studies, we explore uncertainty quantification techniques, robustness evaluations, and risk mitigation strategies. Key themes include Bayesian methods for uncertainty estimation, benchmark studies on prediction reliability, and strategies to handle data scarcity and extrapolation errors. The review highlights gaps in handling adversarial conditions and real-world failures, proposing future directions for more resilient AI frameworks in materials research. By synthesizing these insights, we aim to foster a more cautious and effective use of AI in advancing materials innovation.

This review systematically surveys conceptual approaches to scientific validation in artificial intelligence applications for materials science, drawing exclusively on 50 peer-reviewed publications from 2017 to 2022 to examine how validation is defined, operationalized, critiqued, and innovated upon within the domain. The methodology followed a targeted literature search protocol across Web of Science, Scopus, and arXiv using eight predefined search strings focused on validation, cross-validation, out-of-distribution testing, generalization, and related terms in materials AI, with strict inclusion criteria requiring explicit discussion of conceptual or epistemological aspects of validation and exclusion of purely empirical performance reports, ultimately yielding the 50 selected references after PRISMA-style screening of approximately 250 unique records. Current validation practices in materials AI literature remain anchored in conventional statistical techniques such as random train-test splits, k-fold cross-validation, leave-one-out cross-validation, and hold-out test sets, which the surveyed papers predominantly employ to quantify predictive accuracy on materials property prediction, discovery, and design tasks. Critical findings demonstrate that these practices frequently claim to establish reliable generalization while actually capturing only in-sample performance, systematically overlooking hidden data structures, distribution shifts, feature selection leakage, and the small-data regimes intrinsic to materials science, thereby producing inflated estimates of model utility that do not translate to real-world deployment. To structure the field’s understanding, the review advances a taxonomy of validation approaches organized hierarchically by what they seek to validate—predictive accuracy, robustness, generalizability, and causal structure—providing a conceptual scaffold for aligning methods with task-specific requirements. Recommendations emphasize explicit reporting, justification of method choice, and community-wide benchmarks. At the same time, open challenges persist in areas such as validating generative models for novelty and enabling trustworthy extrapolation beyond training distributions, underscoring an urgent need for epistemologically grounded practices that match the high-stakes demands of materials discovery.

This review systematically examines the treatment of absence and null results in the materials machine learning literature spanning 2017–2022, drawing exclusively on a curated set of 30 peer-reviewed publications and foundational works that address publication bias, negative findings, and reproducibility challenges in data-driven materials discovery. Through a targeted search strategy across databases such as Web of Science, Scopus, and arXiv using terms including “null result,” “negative result,” “publication bias,” “file drawer,” “failed synthesis,” and “reproducibility” combined with materials informatics keywords, the analysis reveals a persistent imbalance: while successful predictions and syntheses dominate published outputs, systematic documentation of failed predictions, unsuccessful syntheses, null correlations, and abandoned model architectures remains exceedingly rare. What is currently reported tends to be limited to negative outcomes that coincidentally reveal mechanistic insights or contradict high-profile hypotheses, whereas what is systematically unreported encompasses the vast majority of unsuccessful hyperparameter searches, negative active learning campaigns, and non-discoveries that yield no novel materials meeting target criteria. The typology of absence and null results developed here identifies six distinct categories—negative predictive outcomes, null hypothesis non-rejection, failed synthesis, non-discovery, failed replication, and abandoned architecture—each carrying unique implications for scientific progress. The consequences of this non-reporting include severe overestimation of model performance, widespread redundant experimental effort, a false sense of methodological consensus across the field, and slowed overall discovery rates as potentially informative negative signals remain invisible. Ultimately, this review offers concrete recommendations for authors, journals, and the broader community to shift incentives toward transparent reporting of absence, thereby restoring balance to the materials AI literature and accelerating reliable data-driven discovery.

Platform-based competition has fundamentally altered the nature of rivalry in digital markets, shifting emphasis from firm-level resources to network effects, multi-sided participation, and ecosystem orchestration. This integrative review synthesizes theoretical perspectives on digital marketplaces, network effects, and ecosystem strategy, drawing on 35 peer-reviewed sources published between 2003 and 2026. It examines how platform market structures differ from traditional competition, the mechanisms through which network effects generate scaling advantages and competitive lock-in, and the strategic role of governance in balancing openness with control. The analysis highlights complementor dynamics, value creation versus capture tensions, and the evolving interplay between platform leaders, users, and complementors. By classifying and comparing core theoretical streams, the review identifies persistent strategic tensions—openness versus control, scale versus governance complexity, and innovation versus appropriation—and traces the maturation of the field from early two-sided market models to contemporary ecosystem perspectives. To advance coherence, the review introduces the platform competition layered synthesis (PCLS) model, a novel integrative architecture that organizes the literature into six interconnected layers. The model reveals feedback mechanisms through which market outcomes continuously reshape platform design and competitive positioning. Implications for digital business strategy and future research directions are discussed.

The field of materials engineering has undergone a profound transformation through the integration of high-throughput computation and data-driven methodologies, evolving from traditional trial-and-error approaches to sophisticated closed-loop systems that accelerate discovery. This review synthesizes recent advancements in computational and data-driven materials ecosystems, focusing on the infrastructure enabling autonomous discovery. Key elements include materials informatics platforms that leverage machine learning for property prediction and inverse design, graph neural networks for representation learning, and high-throughput computational workflows that generate multimodal datasets. We examine the progression from static high-throughput screening to dynamic, closed-loop paradigms incorporating active learning, uncertainty quantification, and simulation-experiment integration. Autonomous laboratories represent a pinnacle of this evolution, where AI orchestrates iterative cycles of hypothesis generation, experimentation, and refinement. The synthesis highlights how these infrastructures bridge computational predictions with experimental validation, fostering inverse materials design and optimizing resource allocation in complex chemical spaces. Challenges in data interoperability and model generalizability are noted, alongside prospects for scalable, self-optimizing systems. Overall, this review positions closed-loop data infrastructures as foundational to next-generation materials engineering, promising accelerated innovation in areas like energy storage, catalysis, and structural materials. By integrating diverse literature, we provide a systems-level perspective on how these tools are reshaping the discovery landscape.

The field of materials science has undergone a transformative shift with the integration of computational and data-driven approaches, particularly through representation learning techniques that enable efficient handling of complex materials data. This review synthesizes recent advancements in architectures for representation learning, encompassing graph neural networks, attention-based models, and physics-inspired embeddings, which facilitate the extraction of meaningful features from diverse data modalities such as atomic structures, stoichiometries, and spectroscopic data. By bridging traditional computational methods with machine learning, these representations have accelerated property prediction, inverse design, and materials discovery applications, addressing challenges in high-dimensional spaces and sparse datasets. The scope of this narrative review covers the evolution from basic informatics to sophisticated multimodal integrations, highlighting how data ecosystems and learning frameworks contribute to autonomous discovery pipelines. A systems-level perspective is adopted to integrate cross-study insights, revealing synergies between representation learning and closed-loop systems that couple simulations with experiments. Looking ahead, the review posits that continued refinement of these architectures will drive scalable, AI-guided materials engineering, fostering innovations in energy, electronics, and structural materials while emphasizing the need for robust, interpretable models in real-world applications.

Computational materials engineering has undergone a transformative shift with the integration of data-driven methodologies and artificial intelligence, enabling accelerated discovery and design of novel materials. Uncertainty quantification (UQ) plays a pivotal role in this paradigm, addressing inherent variabilities in simulations, experimental data, and model predictions to ensure reliable decision-making in materials development. This review synthesizes recent advancements in UQ methods within computational and data-driven materials engineering, focusing on probabilistic modeling, sensitivity analysis, and Bayesian inference techniques deployed across multiscale simulations and machine learning frameworks. We examine deployment contexts ranging from molecular dynamics to additive manufacturing, highlighting how UQ enhances robustness in property prediction, process optimization, and autonomous discovery systems. By integrating insights from high-impact studies the review delineates a systems-level perspective on UQ infrastructures, emphasizing their role in bridging computational predictions with experimental validation. Key challenges such as computational efficiency and data scarcity are contextualized, alongside opportunities for multimodal integration. Ultimately, this synthesis positions UQ as an essential infrastructure for advancing materials informatics toward industrial applicability, offering a forward-looking outlook on scalable, uncertainty-aware workflows in materials engineering.