7 papers
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…
Four regimes of primary radiation damage in tungsten
Jesper Byggmästar, Ville-Markus Yli-Suutala, Aslak Fellman +3
We observe for the first time in silico the transition to a linear regime in the primary damage production in tungsten. As the critical plasma-facing material in fusion reactors, r…
Nine-element machine-learned interatomic potentials for multiphase refractory alloys
Jesper Byggmästar, Tiago Lopes, Zheyong Fan +1
New refractory alloys are being continuously designed and characterised for applications requiring good high-temperature mechanical properties and stability. Computational design f…
Insights Into Radiation Damage in YBaCuO From Machine-Learned Interatomic Potentials
Ashley Dickson, Niccolò Di Eugenio, Federico Ledda +10
Accurate prediction of radiation damage in YBaCuO (YBCO) is essential for assessing the performance of high-temperature superconducting (HTS) tapes in compact fusion…
Recent Advances in Metallic Glasses
Silvia Bonfanti, Ralf Busch, Jesper Byggmästar +12
This paper reviews recent advances in the field of metallic glasses, focusing on the development of novel experimental techniques and in silico models. We discuss progress in exper…
The diffusion-driven orthorhombic to tetragonal transition in YBaCuO derived with a machine learning interatomic potential
Davide Gambino, Niccolò Di Eugenio, Jesper Byggmästar +4
Defects in high temperature superconductors such as YBaCuO (YBCO) critically influence their superconducting behavior, as they substantially degrade or even suppress su…