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 intrinsi
Institute for Advanced Materials Research Press