This article demonstrates the effective use of large language models to create a knowledge graph of materials property relationships, suggesting new applications in machine learning.
In the field of materials science, experimental data or simulation results on material properties are often unevenly distributed. In addition to the vast unexplored material space, properties of lesser interest have not been measured even for well-studied materials, as exemplified by the discovery of the superconductivity of the long-known MgB2. To overcome such challenges, utilizing relationships among material properties based on scientific principles can be beneficial. We have been constructing a knowledge graph of material property relationships using natural language-processing techniques for years. Now, with the surprising development of large language models, constructing a knowledge graph has become much easier. This article explains what a knowledge graph of material property relationships is, presents several types of applications for the knowledge graph, and describes how the constructed knowledge graph can be implemented in machine learning for predicting material property values. We also demonstrate the construction of a knowledge graph of material property relationships and a search system using ChatGPT, without any programming, which will be made publicly available.
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Yoshitake et al. (2025) studied this question.
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