Functional Unit: A New Perspective on Materials Science Research Paradigms

New materials have long marked the civilization level, serving as an impetus for technological progress and societal transformation. The classic structure-property correlations were key of materials science and engineering. However, the knowledge of materials faces significant challenges in adapting to exclusively data-driven approaches for new material discovery. This perspective introduces the concepts of functional units (FUs) to fill the gap in understanding of material structure-property correlations and knowledge inheritance as the "composition-microstructure" paradigm transitions to a data-driven AI paradigm transitions. Firstly, we provide a bird's-eye view of the research paradigm evolution from early "process-structure-properties-performance" to contemporary data-driven AI new trend. Next, we highlight recent advancements in the characterization of functional units across diverse material systems, emphasizing their critical role in multiscale material design. Finally, we discuss the integration of functional units into the new AI-driven paradigm of materials science, addressing both opportunities and challenges in computational materials innovation.

Caveats for the use of Web of Science Core Collection in old literature retrieval and historical bibliometric analysis

By using publications from Web of Science Core Collection (WoSCC), Fosso Wamba and his colleagues published an interesting and comprehensive paper in Technological Forecasting and Social Change to explore the structure and dynamics of artificial intelligence (AI) scholarship. Data demonstrated in Fosso Wamba's study implied that the year 1991 seemed to be a "watershed" of AI research. This research note tried to uncover the 1991 phenomenon from the perspective of database limitation by probing the limitations of search in abstract/author keywords/keywords plus fields of WoSCC empirically. The low availability rates of abstract/author keywords/keywords plus information in WoSCC found in this study can explain the "watershed" phenomenon of AI scholarship in 1991 to a large extent. Some other caveats for the use of WoSCC in old literature retrieval and historical bibliometric analysis were also mentioned in the discussion section. This research note complements Fosso Wamba and his colleagues' study and also helps avoid improper interpretation in the use of WoSCC in old literature retrieval and historical bibliometric analysis.