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Knowledge in materials science is widely dispersed across extensivescientific literature, posing significant challenges to the efficient discoveryand integration of new materials. Traditional methods, often reliant on costlyand time-consuming experimental approaches, further complicate rapidinnovation. Addressing these challenges, the integration of artificialintelligence with materials science has opened avenues for accelerating thediscovery process, though it also demands precise annotation, data extraction,and traceability of information. To tackle these issues, this articleintroduces the Materials Knowledge Graph (MKG), which utilizes advanced naturallanguage processing techniques integrated with large language models to extractand systematically organize a decade's worth of high-quality research intostructured triples, contains 162,605 nodes and 731,772 edges. MKG categorizesinformation into comprehensive labels such as Name, Formula, and Application,structured around a meticulously designed ontology, thus enhancing datausability and integration. By implementing network-based algorithms, MKG notonly facilitates efficient link prediction but also significantly reducesreliance on traditional experimental methods. This structured approach not onlystreamlines materials research but also lays the groundwork for moresophisticated science knowledge graphs.

Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model