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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.

The integration of artificial intelligence (AI) and machine learning (ML) into materials science has accelerated the discovery and design of novel materials by enabling high-throughput prediction of properties from composition, structure, and processing parameters. However, the reliability of these predictions is frequently compromised by uncertainties stemming from limited datasets, model approximations, experimental noise, and intrinsic variability in materials systems. This narrative review synthesizes recent advances in understanding uncertainty and reliability in materials AI. It covers fundamental concepts such as aleatoric and epistemic uncertainty; methods for quantification, including Bayesian neural networks, ensembles, and Gaussian processes; inconsistencies in terminology and language across the literature; and the downstream consequences for decision-making in materials engineering, design, and deployment. Emphasis is placed on calibration of uncertainty estimates, domain-of-applicability assessment, and risk-aware applications in safety-critical contexts such as structural alloys and energy materials. By highlighting best practices and gaps, the review advocates for standardized frameworks to build trust and facilitate industrial translation of materials AI. Key challenges include data scarcity in high-performance materials and the need for physics-informed UQ to mitigate overconfidence in extrapolative predictions. This synthesis underscores the importance of robust uncertainty handling for responsible AI deployment in materials innovation.

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.

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.

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 rapid evolution of computational and data-driven materials engineering has introduced closed-loop systems that integrate simulation, machine learning, and experimental validation to accelerate materials discovery. However, these infrastructures raise critical questions about accountability, encompassing liability distribution across computational workflows, ownership of validation processes, attribution of errors in predictive models, and broader regulatory implications for deployment in high-stakes applications. This review synthesizes recent advancements in uncertainty quantification, error evaluation, and automated frameworks within computational materials ecosystems, highlighting how they underpin accountability mechanisms. We examine liability in multi-stage pipelines where uncertainties propagate from atomic simulations to macroscopic predictions, as seen in neural network potentials and Bayesian active learning approaches. Validation ownership is dissected through ensemble methods and adversarial techniques that assign responsibility for model reliability. Error attribution is explored via metrics and information-theoretic tools that trace discrepancies back to data sources or algorithmic biases. Regulatory considerations are framed around numerical quality controls and convergence protocols essential for certifying computational outputs in sectors like additive manufacturing and thermoelectric materials. By integrating cross-study insights, we propose an original interpretive structure for accountability infrastructures, emphasizing closed-loop feedback as a means to mitigate risks. This synthesis underscores the need for standardized protocols to ensure trustworthy integration of AI-driven tools in materials engineering, paving the way for ethical and reliable innovation.