most citedFrom electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

cond-mat.mtrl-sci2024

Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential

Niklas Leimeroth, Jochen Rohrer, Karsten Albe

Silicon oxycarbides show outstanding versatility due to their highly tunable composition and microstructure. Consequently, a key challenge is a thorough knowledge of structure-prop…

cond-mat.mtrl-sci2024★ 2 cited

From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron

Sarath Menon, Yury Lysogorskiy, Alexander L. M. Knoll +10

We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning P…