A Decade of Digital Twins in Materials Science and Engineering

Digital twins (DTs) are rapidly emerging as transformative tools in materials science and engineering, enabling real-time data integration, predictive modeling, and virtual testing. This study presents a systematic bibliometric review of 1106 peer-reviewed articles published in the last decade in Scopus and Web of Science. Using a five-stage methodology, the review examines publication trends, thematic areas, citation metrics, and keyword patterns. The results reveal exponential growth in scientific output, with Materials Theory, Computation, and Data Science as the most represented area. A thematic analysis of the most cited documents identifies four major research streams: foundational frameworks, DTs in additive manufacturing, sector-specific applications, and intelligent production systems. Keyword co-occurrence and strategic mapping show a strong foundation in modeling, simulation, and optimization, with growing links to machine learning and sustainability. The review highlights current challenges and proposes future research directions for advancing DTs in materials science.