2025-12-18 東京大学
本研究で開発したフレームワーク（材料探索システムの枠組み）の概要図
＜関連情報＞
- https://www.iis.u-tokyo.ac.jp/ja/news/4955/
- https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00618-6
生成AIエージェントによる無機材料設計の加速 Accelerated inorganic materials design with generative AI agents
Izumi Takahara ∙ Teruyasu Mizoguchi ∙ Bang Liu
Cell Reports Physical science Published:December 17, 2025
DOI:https://doi.org/10.1016/j.xcrp.2025.103019
Highlights
- MatAgent couples LLM reasoning with generative diffusion and property prediction models
- Iterative feedback guides crystal generation toward user-defined targets
- Integration of cognitive external tools mimicking human-like reasoning broadens the explored compositional space
- MatAgent facilitates interpretable and property-directed AI-driven materials discovery
Summary
Designing inorganic crystalline materials with tailored properties is critical to technological innovation, yet current generative methods often struggle to efficiently explore desired targets with sufficient interpretability. Here, we present MatAgent, a generative approach for inorganic materials discovery that harnesses the powerful reasoning capabilities of large language models (LLMs). By combining a diffusion-based generative model for crystal structure estimation with a predictive model for property evaluation, MatAgent uses iterative, feedback-driven guidance to steer material exploration precisely toward user-defined targets. Integrated with external cognitive tools—including short-term memory, long-term memory, the periodic table, and a comprehensive knowledge base—MatAgent emulates human expert reasoning to vastly expand the accessible compositional space. Our results demonstrate that MatAgent robustly directs exploration toward desired properties while consistently achieving high compositional validity, uniqueness, and novelty. This framework thus provides a highly interpretable, practical, and versatile AI-driven solution to accelerate the discovery and design of next-generation inorganic materials.
