2 citations · 2 across the 3 of their papers we have counts for
3 papers
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…
Large-scale atomistic study of plasticity in amorphous gallium oxide with a machine-learning potential
Jiahui Zhang, Junlei Zhao, Jesper Byggmästar +2
Compared to the widely investigated crystalline polymorphs of gallium oxide (Ga2O3), knowledge about its amorphous state is still limited. With the help of a machine-learning inter…
Comprehensive structural changes in nanoscale-deformed silicon modelled with an integrated atomic potential
Rafał Abram, Dariusz Chrobak, Jesper Byggmästar +2
In spite of remarkable developments in the field of advanced materials, silicon remains one of the foremost semiconductors of the day. Of enduring relevance to science and technolo…