J. Byggmästar

activity

20192026

Ga_{2} O_{3}

Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials

76 citations · 198 across the 12 of their papers we have counts for

collaborators

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11 papers · 1 filter

cond-mat.mtrl-sci2026

Machine-learned interatomic potential for sputtering of tungsten-boron surfaces

Alexandre Bergero, Jesper Byggmästar, Fredric Granberg

Boronization, where boron is deposited onto tungsten surfaces, is a key technique to reduce plasma contamination, such as oxygen in Tokamak fusion reactors. The exact interaction b…

cond-mat.mtrl-sci2026

Machine-learned interatomic potential for titanium carbide MXenes: Application to ion irradiation simulations

Jesper Byggmästar

A computationally efficient and accurate machine-learned (ML) interatomic potential is developed for bare Ti $_{n + 1}$ C $_{n}$ MXenes. With a diverse set of structures computed with den…

cond-mat.mtrl-sci2024★ 12 cited

Ultrahigh Stability of O-Sublattice in $β$ -Ga $_{2}$ O $_{3}$

Ru He, Junlei Zhao, Jesper Byggmästar +2

Recently reported remarkably high radiation tolerance of $γ$ / $β$ -Ga $_{2}$ O $_{3}$ double-polymorphic structure brings this ultrawide bandgap semiconductor to the frontiers of power elec…

cond-mat.mtrl-sci2024★ 34 cited

Threshold displacement energy map of Frenkel pair generation in $G a_{2} O_{3}$ from machine-learning-driven molecular dynamics simulations

Huan He, Junlei Zhao, Jesper Byggmästar +4

$β$ phase gallium oxide ( $β$ - $G a_{2} O_{3}$ ) demonstrates tremendous potential for electronics applications and offers promising prospects for integration into future space systems…

cond-mat.mtrl-sci2022★ 76 cited

Complex $Ga_{2} O_{3}$ Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials

Junlei Zhao, Jesper Byggmästar, Huan He +3

$Ga_{2} O_{3}$ is a wide-bandgap semiconductor of emergent importance for applications in electronics and optoelectronics. However, vital information of the proper…

cond-mat.mtrl-sci2022

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…

Machine-learned interatomic potential for sputtering of tungsten-boron surfaces

Machine-learned interatomic potential for titanium carbide MXenes: Application to ion irradiation simulations

Ultrahigh Stability of O-Sublattice in β-Ga2​O3​

Threshold displacement energy map of Frenkel pair generation in Ga2​O3​ from machine-learning-driven molecular dynamics simulations

Complex Ga2​O3​ Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials

Comprehensive structural changes in nanoscale-deformed silicon modelled with an integrated atomic potential

Ultrahigh Stability of O-Sublattice in $β$ -Ga $_{2}$ O $_{3}$

Threshold displacement energy map of Frenkel pair generation in $G a_{2} O_{3}$ from machine-learning-driven molecular dynamics simulations

Complex $Ga_{2} O_{3}$ Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials