  Knowledge in materials science is widely dispersed across extensive
scientific literature, posing significant challenges for efficient discovery
and integration of new materials. Traditional methods, often reliant on costly
and time-consuming experimental approaches, further complicate rapid
innovation. Addressing these challenges, the integration of artificial
intelligence with materials science has opened avenues for accelerating the
discovery process, though it also demands precise annotation, data extraction,
and traceability of information. To tackle these issues, this article
introduces the Materials Knowledge Graph (MKG), which utilizes advanced natural
language processing techniques, integrated with large language models to
extract and systematically organize a decade's worth of high-quality research
into structured triples, contains 162,605 nodes and 731,772 edges. MKG
categorizes information into comprehensive labels such as Name, Formula, and
Application, structured around a meticulously designed ontology, thus enhancing
data usability and integration. By implementing network-based algorithms, MKG
not only facilitates efficient link prediction but also significantly reduces
reliance on traditional experimental methods. This structured approach not only
streamlines materials research but also lays the groundwork for more
sophisticated science knowledge graphs.
