Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model
This work addresses the challenge of inefficient materials discovery for researchers in multidisciplinary materials science, representing an incremental advancement by applying existing NLP methods to a new domain-specific dataset.
The paper tackled the problem of dispersed knowledge in materials science by constructing a Materials Knowledge Graph (MKG) using large language models to extract and organize data from a decade of research, resulting in a graph with 162,605 nodes and 731,772 edges that enhances data usability and reduces reliance on traditional experimental methods.
Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges to the 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.