76 citations · 217 across the 13 of their papers we have counts for
13 papers
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…
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 TiC MXenes. With a diverse set of structures computed with den…
Ultrahigh Stability of O-Sublattice in -GaO
Ru He, Junlei Zhao, Jesper Byggmästar +2
Recently reported remarkably high radiation tolerance of /-GaO double-polymorphic structure brings this ultrawide bandgap semiconductor to the frontiers of power elec…
Threshold displacement energy map of Frenkel pair generation in from machine-learning-driven molecular dynamics simulations
Huan He, Junlei Zhao, Jesper Byggmästar +4
phase gallium oxide (-) demonstrates tremendous potential for electronics applications and offers promising prospects for integration into future space systems…
Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten
Jiahui Liu, Jesper Byggmastar, Zheyong Fan +2
Simulating collision cascades and radiation damage poses a long-standing challenge for existing interatomic potentials, both in terms of accuracy and efficiency. Machine-learning b…
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…
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…
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…
Energy loss in low energy nuclear recoils in dark matter detector materials
Sebastian Sassi, Matti Heikinheimo, Kimmo Tuominen +4
Recent progress in phonon-mediated detectors with eV-scale nuclear recoil energy sensitivity requires an understanding of the effect of the crystalline defects on the energy spectr…
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…
Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials: defects and segregation
Jesper Byggmästar, Kai Nordlund, Flyura Djurabekova
We develop a fast and accurate machine-learned interatomic potential for the Mo-Nb-Ta-V-W quinary system and use it to study segregation and defects in the body-centred cubic refra…
Phase Transition of Two-Dimensional Ferroelectric and Paraelectric Ga2O3 Monolayer: A Density Functional Theory and Machine-Learning Study
Junlei Zhao, Jesper Byggmastar, Zhaofu Zhang +3
Ga2O3 is a wide-band-gap semiconductor of great interest for applications in electronics and optoelectronics. Two-dimensional (2D) Ga2O3 synthesized from top-down or bottom-up proc…
On the classification and quantification of crystal defects after energetic bombardment by machine learned molecular dynamics simulations
F. J. Domínguez-Gutiérrez, J Byggmästar, K. Nordlund +2
The analysis of the damage on plasma facing materials (PFM), due to its direct interaction with the plasma environment, is needed to build the next generation of nuclear machines,…