Construction of Functional Materials Knowledge Graph in
  Multidisciplinary Materials Science via Large Language Model
Yanpeng Ye Jie Ren Shaozhou Wang Yuwei Wan Imran Razzak Tong Xie Wenjie Zhang
The convergence of materials science and artificial intelligence has unlocked
new opportunities for gathering, analyzing, and generating novel materials
sourced from extensive scientific literature. Despite the potential benefits,
persistent challenges such as manual annotation, precise extraction, and
traceability issues remain. Large language models have emerged as promising
solutions to address these obstacles. This paper introduces Functional
Materials Knowledge Graph (FMKG), a multidisciplinary materials science
knowledge graph. Through the utilization of advanced natural language
processing techniques, extracting millions of entities to form triples from a
corpus comprising all high-quality research papers published in the last
decade. It organizes unstructured information into nine distinct labels,
covering Name, Formula, Acronym, Structure/Phase, Properties, Descriptor,
Synthesis, Characterization Method, Application, and Domain, seamlessly
integrating papers' Digital Object Identifiers. As the latest structured
database for functional materials, FMKG acts as a powerful catalyst for
expediting the development of functional materials and a fundation for building
a more comprehensive material knowledge graph using full paper text.
Furthermore, our research lays the groundwork for practical text-mining-based
knowledge management systems, not only in intricate materials systems but also
applicable to other specialized domains.