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説明:

In the field of materials science, experimental data or simulation results on material prop ertites 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.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: materials property relationship, knowledge graph, graph search, data interpolation, generative AI

刊行年月日: 2025-09-28

出版者: MDPI AG

掲載誌:

• Applied Sciences (ISSN: 20763417) vol. 15 issue. 19 10511

研究助成金:

• Japan Science and Technology Agency (JST) Mirai Program JPMJMI21G2

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.3390/app151910511

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更新時刻: 2025-11-04 12:30:07 +0900

MDRでの公開時刻: 2025-11-04 12:27:50 +0900