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Description:

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.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: materials property relationship, knowledge graph, graph search, data interpolation, generative AI

Date published: 2025-09-28

Publisher: MDPI AG

Journal:

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

Funding:

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

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.3390/app151910511

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Updated at: 2025-11-04 12:30:07 +0900

Published on MDR: 2025-11-04 12:27:50 +0900