279 citations · 588 across the 10 of their papers we have counts for
5 papers · 1 filter
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 (…
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…
Data-driven study of the enthalpy of mixing in the liquid phase
Guillaume Deffrennes, Bengt Hallstedt, Taichi Abe +5
The enthalpy of mixing in the liquid phase is a thermodynamic property reflecting interactions between elements that is key to predict phase transformations. Widely used models exi…
A machine learning-based classification approach for phase diagram prediction
Guillaume Deffrennes, Kei Terayama, Taichi Abe +1
Knowledge of phase diagrams is essential for material design as it helps in understanding microstructure evolution during processing. The determination of phase diagrams is thus on…
Efficient Construction Method for Phase Diagrams Using Uncertainty Sampling
Kei Terayama, Ryo Tamura, Yoshitaro Nose +4
We develop a method to efficiently construct phase diagrams using machine learning. Uncertainty sampling (US) in active learning is utilized to intensively sample around phase boun…