Wataru Takahara of the Materials Informatics Laboratory received the MRS MT01 Best Presentation Award at the 2026 MRS Spring Meeting & Exhibit

Summary

In May 2026, Wataru Takahara of the Materials Informatics Laboratory at Nara Institute of Science and Technology received the MRS MT01 Best Presentation Award for his presentation at the MT01 Symposium entitled “Interfacing AI with Prior Knowledge and Human Expertise for Data-Efficient Autonomous Materials Research,” at the 2026 MRS Spring Meeting & Exhibit. He was a third-year doctoral student with a full-time job when the presentation was accepted and had become an alumnus by the time of the presentation. The meeting was held in Honolulu, Hawaii, USA, from April 26 to May 1, 2026. This award recognizes outstanding presentations delivered in the symposium.

Presentation title

MDSK-RAG—Materials Dual-Source Knowledge Retrieval-Augmented Generation for Local Large Language Models—Demonstration on Photocatalysts

Authors

Wataru Takahara^A, Yuichi Yamaguchi^B, Mai Ogano^B, Fuga Kakami^B, Yosuke Harashima^A, Tomoaki Takayama^A, Shogo Takasuka^A, Akihiko Kudo^B, Mikiya Fujii^A

A: NAIST, B: Tokyo University of Science

Research detail

This research proposes a framework for utilizing large language models (LLMs) in materials development by integrating experimental data and scientific literature on metal sulfide photocatalysts. By applying the proposed method to local LLMs, the framework enables domain specialization using relatively small datasets while maintaining the confidentiality of research data. It also allows continuous updating of knowledge sources and flexible extension without modifying the base model. This work demonstrates the potential of LLMs in materials development by combining experimental facts with knowledge from the scientific literature, even in limited-data environments.

Comment

I am deeply honored to receive the MRS MT01 Best Presentation Award at the 2026 MRS Spring Meeting & Exhibit. I would like to express my sincere gratitude to all of my collaborators for their invaluable guidance and support throughout this research. Encouraged by this award, I will continue to contribute to the advancement of AI for Materials and AI for Science.