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5 papers · 2 filters
Complex Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials
Junlei Zhao, Jesper Byggmästar, Huan He +3
is a wide-bandgap semiconductor of emergent importance for applications in electronics and optoelectronics. However, vital information of the proper…
Efficient atomistic simulations of radiation damage in W and W-Mo using machine-learning potentials
Mikko Koskenniemi, Jesper Byggmästar, Kai Nordlund +1
The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential that was recently extended to include the description of radiation effects. In this…
Biased self-diffusion on Cu surface due to electric field gradients
Jyri Kimari, Ye Wang, Andreas Kyritsakis +2
Under strong electric fields, an arc of strong current flowing through plasma can link two metal surfaces even in ultra high vacuum. Despite decades of research, the chain of event…
Simple machine-learned interatomic potentials for complex alloys
Jesper Byggmästar, Kai Nordlund, Flyura Djurabekova
Developing data-driven machine-learning interatomic potentials for materials containing many elements becomes increasingly challenging due to the vast configuration space that must…
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…