3 papers
cond-mat.mtrl-sci2026
LLM-guided phase diagram construction through high-throughput experimentation
Ryo Tamura, Haruhiko Morito, Yuna Oikawa +7
Constructing phase diagrams for multicomponent alloys requires extensive experimental measurements and is a time-consuming task. Here we investigate whether large language models (…
cond-mat.mtrl-sci2026
ZEBRA-Prop: A Zero-Shot Embedding-Based Rapid and Accessible Regression Model for Materials Properties
Ryoma Yamamoto, Akira Takahashi, Kei Terayama +2
Large language models (LLMs) exhibit substantial potential across diverse scientific disciplines, including materials science. A property prediction framework, ZEBRA-Prop (Zero-Sho…
physics.comp-ph2026
Update of PHYSBO: Improving Usability and Portability of Bayesian Optimization for Physics and Materials Research
Yuichi Motoyama, Kazuyoshi Yoshimi, Tatsumi Aoyama +3
Bayesian optimization (BO) is widely used to accelerate physics and materials research, where objective function evaluations are computationally or experimentally expensive. While…