Izumi Takahara

Alumni

Publications

Accelerated Inorganic Materials Design with Generative Al Agents

Teruyasu Mizoguchi

Designing inorganic crystalline materials with tailored properties is critical to technological innovation, yet current generative computati… (voir plus)onal 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 materials 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 material 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.

2025-11-30

Cell Reports Physical Science (publié)

doi.org

arxiv.org

Accelerated Inorganic Materials Design with Generative AI Agents

Izumi Takahara

Teruyasu Mizoguchi

Bang Liu

2025-09-19

NeurIPS.cc/2025/Workshop/AI4Mat (poster)

openreview.net