24 citations · 26 across the 3 of their papers we have counts for
3 papers
Machine-learning interatomic potentials from a users perspective: A comparison of accuracy, speed and data efficiency
Niklas Leimeroth, Linus C. Erhard, Karsten Albe +1
Machine learning interatomic potentials (MLIPs) have massively changed the field of atomistic modeling. They enable the accuracy of density functional theory in large-scale simulat…
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…
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…