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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 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.