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.

翻译：材料科学知识广泛分散于大量科学文献中，这对新材料的有效发现与整合构成了重大挑战。传统方法通常依赖于成本高昂且耗时的实验手段，进一步阻碍了快速创新。为应对这些挑战，人工智能与材料科学的融合为加速发现进程开辟了新途径，尽管这也要求对信息进行精确标注、数据提取与溯源。针对这些问题，本文提出了材料知识图谱（MKG），它利用先进的自然语言处理技术，并结合大型语言模型，从十年间的高质量研究中提取并系统化组织信息，构建为结构化三元组，共包含162,605个节点和731,772条边。MKG将信息分类为名称、分子式、应用等综合性标签，并围绕精心设计的本体进行结构化，从而提升了数据的可用性与整合度。通过实施基于网络的算法，MKG不仅促进了高效的链接预测，还显著降低了对传统实验方法的依赖。这种结构化方法不仅简化了材料研究流程，也为构建更复杂的科学知识图谱奠定了基础。