279 citations · 588 across the 10 of their papers we have counts for
11 papers
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…
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…
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…
Individual health-disease phase diagrams for disease prevention based on machine learning
Kazuki Nakamura, Eiichiro Uchino, Noriaki Sato +8
Early disease detection and prevention methods based on effective interventions are gaining attention. Machine learning technology has enabled precise disease prediction by capturi…
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…
Bayesian optimization package: PHYSBO
Yuichi Motoyama, Ryo Tamura, Kazuyoshi Yoshimi +3
PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization. It has been developed mainly for application…
Deep learning-based quality filtering of mechanically exfoliated 2D crystals
Yu Saito, Kento Shin, Kei Terayama1 +7
Two-dimensional (2D) crystals are attracting growing interest in various research fields such as engineering, physics, chemistry, pharmacy and biology owing to their low dimensiona…
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…
Population-based de novo molecule generation, using grammatical evolution
Naruki Yoshikawa, Kei Terayama, Teruki Honma +2
Automatic design with machine learning and molecular simulations has shown a remarkable ability to generate new and promising drug candidates. Current models, however, still have p…
ChemTS: An Efficient Python Library for de novo Molecular Generation
Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe +2
Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of…