1 citations · 1 across the 3 of their papers we have counts for
3 papers
cond-mat.mtrl-sci2026
Competing Hydrogenation Pathways to Metastable CaH Revealed by Machine-Learning-Potential Molecular Dynamics
Ryuhei Sato, Peter I. C. Cooke, Maélie Caussé +7
The synthesis of the high- superhydride CaH has stimulated significant interest in understanding synthesis pathways for metastable hydrides. However, the microscopic mecha…
cond-mat.mtrl-sci2025
douka: A universal platform of data assimilation for materials modeling
Aoi Watanabe, Ryuhei Sato, Ikuya Kinefuchi +1
A large-scale, general-purpose data assimilation (DA) platform for materials modeling, douka, was developed and applied to nonlinear materials models. The platform demonstrated its…
cond-mat.mtrl-sci2020★ 1 cited
Prediction of mechanical properties of non-equiatomic high-entropy alloy by atomistic simulation and machine learning
Liang Zhang, Kun Qian, Björn W. Schuller +3
High-entropy alloys (HEAs) with multiple constituent elements have been extensively studied in the past 20 years due to their promising engineering application. Previous experiment…