76 citations · 217 across the 13 of their papers we have counts for
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cond-mat.mtrl-sci2021★ 6 cited
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…
cond-mat.mtrl-sci2021★ 19 cited
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…