11 citations · 32 across the 10 of their papers we have counts for
9 papers · 1 filter
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…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…
Accelerated Inorganic Materials Design with Generative AI Agents
Izumi Takahara, Teruyasu Mizoguchi, Bang Liu
Designing inorganic crystalline materials with tailored properties is critical to technological innovation, yet current generative computational methods often struggle to efficient…
Generative Inverse Design of Crystal Structures via Diffusion Models with Transformers
Izumi Takahara, Kiyou Shibata, Teruyasu Mizoguchi
Recent advances in deep learning have enabled the generation of realistic data by training generative models on large datasets of text, images, and audio. While these models have d…
Crystal orbital overlap population based on all-electron ab initio simulation with numeric atom-centered orbitals and its application to chemical-bonding analysis in Li-intercalated layered materials
Izumi Takahara, Kiyou Shibata, Teruyasu Mizoguchi
Crystal Orbital Overlap Population (COOP) is one of the effective tools for chemical-bonding analysis, and thus it has been utilized in the materials development and characterizati…