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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 advent of digital twins has revolutionized various engineering domains, yet their application in materials science often relies heavily on computationally intensive simulations to replicate physical behaviors. This conceptual paper introduces “Digital Materials Twins” (DMTs) as a novel paradigm that eschews traditional simulation in favor of purely data-driven representations. DMTs leverage artificial intelligence and machine learning to create virtual counterparts of materials based solely on empirical data, enabling efficient prediction and analysis without physics-based modeling. Drawing on recent advances in data-driven materials science, we define DMTs as dynamic, data-centric models that capture material properties, structures, and responses by learning from diverse datasets. We delineate their boundaries, emphasizing limitations in real-time dynamics and in extrapolation beyond the trained data regime. By synthesizing the literature on digital twins and AI in materials, we propose a conceptual framework comprising data ingestion, feature extraction, model training, and inference. This framework enables use cases in accelerated materials design, property prediction, and optimization across sectors such as energy storage and additive manufacturing. By prioritizing conceptual innovation over empirical validation, this blueprint aims to guide future theoretical developments and foster scalable, simulation-free approaches to materials innovation. The implications for high-impact applications in applied artificial intelligence are discussed, highlighting DMTs’ potential to democratize materials research.

The rapid integration of artificial intelligence (AI) into materials science marks a profound shift in how materials are discovered, characterized, and optimized. Rather than functioning merely as a computational aid, AI increasingly operates as an epistemic instrument that reshapes scientific workflows, decision-making practices, and notions of explanation within the field. This narrative review examines the conceptual foundations underpinning applied AI in materials science, with a particular focus on core definitions, implicit and explicit assumptions, and unresolved debates that continue to shape the domain. Key AI paradigms—including supervised, unsupervised, and reinforcement learning—are situated within materials-specific contexts such as property prediction, structure–property mapping, and autonomous experimentation. The review critically interrogates foundational assumptions regarding data quality, representativeness, generalization, and model transferability, highlighting how these assumptions condition both the successes and failures of AI-driven materials research. Persistent debates surrounding interpretability, epistemic trust, ethical responsibility, and environmental sustainability are synthesized from recent literature published. By articulating both the transformative potential and the conceptual limitations of applied AI, this review underscores the necessity of rigorous validation, transparent reasoning, and interdisciplinary collaboration to ensure that AI contributes robustly and responsibly to materials innovation.

In the rapidly evolving domain of artificial intelligence for materials science, path dependency remains a critically overlooked phenomenon that shapes both computational pipelines and the broader scientific enterprise. Algorithmic path dependency manifests when seemingly innocuous early choices in neural network initialization, training data ordering, hyperparameter selection, feature descriptor definition, or early stopping criteria create irreversible constraints on subsequent model behaviors and outputs, as evidenced in recurrent neural network architectures designed for heterogeneous materials. Scientific path dependency, by contrast, arises in the history and philosophy of science when initial decisions regarding research problems, material systems, theoretical frameworks, experimental protocols, or funding priorities lock research communities into particular trajectories, rendering alternative avenues increasingly difficult to pursue even when they might yield superior insights. This paper advances the theoretical claim that algorithmic path dependency propagates directly into scientific path dependency within materials AI, such that technical decisions made at the level of code and data become de facto determinants of which materials are discovered, which questions are asked, and which knowledge ultimately enters the scientific canon. The linkage operates through identifiable mechanisms, including output filtering, resource allocation, knowledge representation, and publication bias, each amplifying the long-term scientific consequences of early algorithmic commitments. By drawing upon foundational economic concepts of increasing returns and historical contingency alongside contemporary literature in machine learning for materials, this theoretical analysis proposes that materials AI researchers must explicitly recognize these dynamics to avoid unintended lock-in effects that could limit the diversity and robustness of future discoveries. The analysis further derives corollaries concerning constrained output diversity, the practical irreversibility of certain scientific paths, and the necessity of methodological pluralism, offering concrete implications for research practice, peer review standards, and community norms. Ultimately, this conceptual linkage reframes early algorithmic decisions not as mere technical details but as foundational scientific commitments whose consequences reverberate through the entire materials discovery ecosystem.

In the rapidly evolving field of computational and data-driven materials engineering, the interplay between algorithmic processes and established scientific paradigms shapes the reliability of predictive outcomes. Traditional scientific consensus emerges from iterative experimental validation, peer review, and cumulative evidence, fostering a shared understanding of material behaviors and properties. In contrast, algorithmic consensus arises from the aggregation of computational models, often leveraging machine learning architectures to distill patterns from vast datasets. This manuscript explores the tensions and synergies between these two forms of consensus in materials prediction, highlighting how data-driven approaches can either reinforce or challenge longstanding scientific interpretations. A conceptual gap persists in integrating these consensus mechanisms, where algorithmic outputs may diverge from empirical benchmarks due to representation biases or uncertainty propagation. To address this, we introduce the Consensus Integration Lattice (CIL), a novel framework that structures the alignment of algorithmic and scientific consensus through layered computational workflows, feedback mechanisms, and epistemic risk assessments. By conceptualizing discovery pipelines that couple high-throughput simulations with multimodal data integration, CIL facilitates more robust materials predictions. Implications extend to autonomous discovery systems, inverse design strategies, and uncertainty quantification, potentially enhancing the efficiency of materials informatics ecosystems. This work underscores the need for infrastructure-level analyses to bridge computational agility with scientific rigor, paving the way for hybrid paradigms in materials engineering.

In the rapidly evolving field of computational and data-driven materials engineering, machine learning models are increasingly trained on curated datasets that represent a closed-world approximation of material properties and behaviors. However, the broader materials universe encompasses vast, unexplored compositional spaces, dynamic environmental interactions, and emergent phenomena that defy static boundaries. This conceptual manuscript addresses the inherent tension between closed-world training paradigms—characterized by finite, labeled data regimes—and the open, infinite nature of materials discovery. We introduce a novel conceptual framework, termed the Adaptive Boundary Inference Architecture (ABIA), which integrates representation learning, uncertainty-aware feedback mechanisms, and multi-scale inference logics to navigate this disparity. ABIA conceptualizes training as a dynamic process where model boundaries adapt through iterative interactions between data representations and discovery pipelines, fostering resilience to out-of-distribution materials. By synthesizing recent advances in graph neural networks, foundation models, and autonomous systems, the framework highlights computational steering strategies that balance exploitation of known data with exploration of open spaces. Implications extend to enhanced inverse design, multimodal integration, and epistemic risk management in materials informatics, ultimately advancing sustainable and efficient materials engineering workflows. This work underscores the need for interpretive systems that transcend traditional closed-loop constraints, promoting a more holistic approach to data-driven discovery in an unbounded materials landscape.

In the evolving landscape of computational and data-driven materials engineering, the integration of machine learning and high-throughput methodologies has transformed traditional materials discovery into sophisticated algorithmic processes. This shift emphasizes the need to reframe materials selection algorithms as discovery recommendation systems, where predictive models serve not merely as classifiers but as dynamic recommenders guiding exploration across vast chemical spaces. A conceptual gap persists in how these systems handle the interplay between representation learning, uncertainty quantification, and closed-loop feedback, often leading to suboptimal navigation of multimodal datasets. To address this, we introduce the Adaptive Discovery Recommendation Architecture (ADRA), a novel framework that conceptualizes materials selection as a recommendation engine optimized for epistemic steering in inverse design workflows. ADRA incorporates layered computational logics that balance representation fidelity with inference adaptability, enabling seamless coupling of simulation and experimental data streams. By reframing algorithms through recommendation paradigms, ADRA highlights infrastructure trade-offs in scalability and interpretability, fostering more robust discovery pipelines. Implications extend to materials informatics ecosystems, enhancing autonomous systems in high-throughput computation and foundation models for science. This conceptual reframing underscores the potential for recommendation-based steering to mitigate epistemic risks, ultimately advancing data-driven innovation in materials engineering.

In the evolving landscape of computational and data-driven materials engineering, iterative learning systems have become pivotal for accelerating materials discovery through integrated machine learning pipelines and high-throughput computations. These systems, encompassing active learning loops and closed-loop experimentation, rely on dynamic representations of materials properties and structures to guide successive iterations of model refinement and data acquisition. However, a critical yet underexplored phenomenon emerges: representation drift, where iterative updates inadvertently alter the semantic fidelity of learned embeddings, potentially leading to misaligned inferences across discovery cycles. This conceptual manuscript identifies this gap within materials informatics ecosystems, highlighting how drift manifests in graph neural networks, multimodal datasets, and uncertainty-aware frameworks. To address this, we introduce the Iterative Representation Stabilization Framework (IRSF), a novel conceptual architecture that integrates stabilization mechanisms across data ingestion, model adaptation, and inference steering layers. IRSF conceptualizes drift as a systemic interaction between feedback loops and representation spaces, offering interpretive insights into maintaining epistemic consistency in autonomous discovery workflows. Implications extend to enhancing the robustness of foundation models for science, simulation-experiment couplings, and inverse design paradigms, fostering more reliable computational steering in materials engineering. By framing representation drift through infrastructure-level trade-offs, this work provides a foundational lens for interpreting iterative dynamics, ultimately supporting sustainable advancements in data-driven materials paradigms.