2025-12-10 物質・材料研究機構,筑波大学科学技術振興機構
図: 人間による研究コミュニティと自律自動AIネットワークの比較。 (a) 異分野の研究者達が、コミュニケーションによって幅広いナレッジを共有することで研究者ネットワークを形成し、新規材料探索を推進。 (b) 異なる材料を探索する自律自動AIシステムが、自発的にナレッジを共有することで自律自動AIネットワークを形成し、新規材料探索を推進。
＜関連情報＞
- https://www.nims.go.jp/press/2025/12/202512100.html
- https://www.nims.go.jp/press/2025/12/o1asj7000000956y-att/202512100.pdf
- https://www.nature.com/articles/s41524-025-01851-8
転移学習による自律的材料探査システムのネットワーク化 Networking autonomous material exploration systems through transfer learning
Naoki Yoshida,Yutaro Iwabuchi,Yasuhiko Igarashi & Yuma Iwasaki
npj Computational Materials Published:09 December 2025
DOI:https://doi.org/10.1038/s41524-025-01851-8
Abstract
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.
