11 citations · 32 across the 10 of their papers we have counts for
10 papers
Toward Auditable AI Scientists: A Hypothesis Evolution Protocol for LLM Agents
Izumi Takahara, Teruyasu Mizoguchi
Large language model (LLM) agents are increasingly expected to play a central role in AI-driven scientific discovery. Equipped with broad knowledge, flexible reasoning, and tool us…
Scale-Dependent Input Representation and Confidence Estimation for LLMs in Materials Property Prediction
Shuichiro Ozawa, Izumi Takahara, Teruyasu Mizoguchi
Large language models (LLMs) are increasingly applied to materials science. However, the relationship between prediction accuracy, input representation, and model scale remains unc…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Inverse Materials Design via Joint Generation of Crystal Structures and Local Electronic Descriptors
Ibuki Okuda, Izumi Takahara, Teruyasu Mizoguchi
Inverse design of inorganic crystals, in which structures are generated to satisfy a target property while preserving diversity and physical plausibility, remains more demanding th…
Generative Inverse Estimation of 3D Atomic Coordination from Near-Edge Spectra via Equivariant Diffusion Models
Ren Okubo, Yu Fujikata, Izumi Takahara +1
Extracting 3D atomic coordinates from spectroscopic data is a longstanding inverse problem. We present an equivariant diffusion model that generates site-specific 3D structures dir…
Towards Agentic Intelligence for Materials Science
Huan Zhang, Yizhan Li, Wenhao Huang +18
The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-iso…