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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.