49 citations · 51 across the 4 of their papers we have counts for
4 papers
Computing binary alloy phase diagrams with explicit configurational and vibrational entropy
Sarath Menon, Marvin Poul, Tilmann Hickel +2
Phase stability in multicomponent solid solutions depends on configurational entropy beyond the ideal mixing limit, but capturing it together with vibrational entropy within the sa…
Data-Efficient Training of Linear ACE Potentials through Leverage-Guided Subset Selection of ASSYST Structure Pools
Aynour Khosravi, Marvin Poul, Jörg Neugebauer +1
The construction of machine-learned interatomic potentials (MLIPs) is often limited by the cost of generating large density-functional-theory (DFT) training datasets. For systemati…
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…
Systematic Atomic Structure Datasets for Machine Learning Potentials: Application to Defects in Magnesium
Marvin Poul, Liam Huber, Erik Bitzek +1
We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration o…