Izumi Takahara

Towards Agentic Intelligence for Materials Science

Ziyu Hou

Yu Song

Xuye Liu

Jinya Jiang

Leonard R. MacGillivray

Teruyasu Mizoguchi

Tianshu Yu

Lizi Liao

Yuyu Luo

Yu Rong

Jia LI

Ying Diao

Heng Ji … (voir 1 de plus)

Bang Liu

The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in d… (voir plus)iscovery requires moving beyond task-isolated, fine-tuned models toward agentic systems that plan, act, and learn across the full discovery loop. This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining, through domain adaptation and instruction tuning, to goal-conditioned agents interfacing with simulation and experimental platforms. Unlike prior reviews, we treat the entire process as an end-to-end system to be optimized for tangible discovery outcomes rather than proxy benchmarks. This perspective allows us to trace how upstream design choices-such as data curation and training objectives-can be aligned with downstream experimental success through effective credit assignment. To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science. We then analyze the field through two focused lenses: From the AI perspective, the survey details LLM strengths in pattern recognition, predictive analytics, and natural language processing for literature mining, materials characterization, and property prediction; from the materials science perspective, it highlights applications in materials design, process optimization, and the acceleration of computational workflows via integration with external tools (e.g., DFT, robotic labs). Finally, we contrast passive, reactive approaches with agentic design, cataloging current contributions while motivating systems that pursue long-horizon goals with autonomy, memory, and tool use. This survey charts a practical roadmap towards autonomous, safety-aware LLM agents aimed at discovering novel and useful materials.

2025-12-31

arXiv (prépublication)

doi.org

arxiv.org

Accelerated Inorganic Materials Design with Generative Al Agents

Izumi Takahara

Teruyasu Mizoguchi

Bang Liu

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