49 citations · 65 across the 6 of their papers we have counts for
6 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…
Triple Junctions as Dislocation-Like Defects: The Role of Grain Boundary Crystallography Revealed by Experiment and Atomistic Simulation
Tobias Brink, Saba Saood, Peter Schweizer +2
Grain boundary networks and their evolution are strongly influenced by triple junctions. The defect nature of these line defects significantly affects the properties of the network…
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…
Automated optimization of convergence parameters in plane wave density functional theory calculations via a tensor decomposition-based uncertainty quantification
Jan Janssen, Edgar Makarov, Tilmann Hickel +2
First principles approaches have revolutionized our ability in using computers to predict, explore and design materials. A major advantage commonly associated with these approaches…