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説明:

Autonomous material exploration systems that integrate robotics, material simulations, and machine learning have advanced rapidly in recent years. Although their number continues to grow, these systems currently operate in isolation, limiting the overall efficiency of autonomous material discovery. In analogy to how human researchers advance materials science by sharing knowledge and collaborating, autonomous systems can also benefit from networking and knowledge exchange. Here, we propose a framework in which multiple autonomous material exploration systems form a network via transfer learning, selectively utilizing relevant knowledge from other systems in real time. We demonstrate this approach using three distinct autonomous systems and show that such networking significantly enhances the efficiency of material discovery. Our results suggest that the proposed framework can enable the development of large-scale autonomous material exploration networks, ultimately accelerating progress in material development.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: machine learning

刊行年月日: 2025-12-09

出版者: Springer Science and Business Media LLC

掲載誌:

• npj Computational Materials (ISSN: 20573960) vol. 11 issue. 1

研究助成金:

• JST-CREST JPMJCR21O1
• JST-CREST JPMJCR21O1
• JST-CREST JPMJCR21O1
• JST-CREST JPMJCR21O1

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1038/s41524-025-01851-8

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更新時刻: 2025-12-13 08:30:26 +0900

MDRでの公開時刻: 2025-12-13 08:22:19 +0900