3 papers
cond-mat.soft2025
Bayesian sparse modeling for interpretable prediction of hydroxide ion conductivity in anion-conductive polymer membranes
Ryo Murakami, Kenji Miyatake, Ahmed Mohamed Ahmed Mahmoud +2
Anion-conductive polymer membranes have attracted considerable attention as solid electrolytes for alkaline fuel cells and electrolysis cells. Their hydroxide ion conductivity vari…
cond-mat.mtrl-sci2024
Rapid and Robust construction of an ML-ready peak feature table from X-ray diffraction data using Bayesian peak-top fitting
Ryo Murakami, Taisuke T. Sasaki, Hideki Yoshikawa +5
To advance the development of materials through data-driven scientific methods, appropriate methods for building machine learning (ML)-ready feature tables from measured and comput…
cond-mat.mtrl-sci2023
Bayesian inference to identify crystalline structures for XRD
Ryo Murakami, Yoshitaka Matsushita, Kenji Nagata +2
Crystalline phase structure is essential for understanding the performance and properties of a material. Therefore, this study identified and quantified the crystalline phase struc…