26 citations · 30 across the 5 of their papers we have counts for
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Effects of training machine-learning potentials for radiation damage simulations using different pseudopotentials
A. Fellman, J. Byggmästar, F. Granberg +2
Machine learning (ML) has become a commonplace approach in the development of interatomic potentials for molecular dynamics simulations, and its use also for radiation effect model…
Radiation damage and phase stability of AlCrCuFeNi alloys using a machine-learned interatomic potential
Aslak Fellman, Jesper Byggmästar, Fredric Granberg +2
We develop a machine-learned interatomic potential for AlCrCuFeNi high-entropy alloys (HEA) using a diverse set of structures from density functional theory calculated including ma…
Nanoscale structure formation in nickel-aluminum alloys synthesized far from equilibrium
Zhehao Chen, Aslak J J Fellman, Katarzyna Mulewska +9
The present study reports on the structure formation in thin epitaxial nickel-aluminum films (Ni1-xAlx; Al atomic fraction x up to x=0.24) grown on MgO(001) substrates by magnetron…
Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni
Aslak Fellman, Jesper Byggmästar, Fredric Granberg +2
Machine learning (ML) has become widely used in the development of interatomic potentials for molecular dynamics simulations. However, most ML potentials are still much slower than…
Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron
Jesper Byggmästar, Giorgos Nikoulis, Aslak Fellman +3
We develop and compare four interatomic potentials for iron: a simple machine-learned embedded atom method (EAM) potential, a potential with machine-learned two- and three-body-dep…