150 citations · 372 across the 13 of their papers we have counts for
14 papers
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…
Radiation damage and phase stability of AlCrCuFeNi alloys using a machine-learned interatomic potential
Aslak Fellman, Jesper Byggmästar, Fredric Granberg +2
We develop a machine-learned interatomic potential for AlCrCuFeNi high-entropy alloys (HEA) using a diverse set of structures from density functional theory calculated including ma…
Threshold displacement energies in refractory high-entropy alloys
Jesper Byggmästar, Flyura Djurabekova, Kai Nordlund
Refractory high-entropy alloys show promising resistance to irradiation, yet little is known about the fundamental nature of radiation-induced defect formation. Here, we simulate t…
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…
Enhancing Irradiation Resistance in Refractory Medium Entropy Alloys with Simplified Chemistry
M. A. Tunes, D. Parkison, B. Sun +12
Refractory High-Entropy Alloys (RHEAs) hold promising potential to be used as structural materials in future nuclear fusion reactors, where W and its alloys are currently leading c…
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…
Atomistic simulations of nanoindentation in single crystalline tungsten: The role of interatomic potentials
F. J. Dominguez-Gutierrez, P. Grigorev, A. Naghdi +6
Computational modeling is usually applied to aid experimental exploration of advanced materials to better understand the fundamental plasticity mechanisms during mechanical testing…
Simple machine-learned interatomic potentials for complex alloys
Jesper Byggmästar, Kai Nordlund, Flyura Djurabekova
Developing data-driven machine-learning interatomic potentials for materials containing many elements becomes increasingly challenging due to the vast configuration space that must…
Gaussian approximation potentials for body-centered-cubic transition metals
Jesper Byggmästar, Kai Nordlund, Flyura Djurabekova
We develop a set of machine-learning interatomic potentials for elemental V, Nb, Mo, Ta, and W using the Gaussian approximation potential framework. The potentials show good accura…
Dynamical stability of radiation-induced C15 clusters in iron
Jesper Byggmästar, Fredric Granberg
Density functional theory predicts clusters in the form of the C15 Laves phase to be the most stable cluster of self-interstitials in iron at small sizes. The C15 clusters can form…
Machine-learning interatomic potential for radiation damage and defects in tungsten
Jesper Byggmästar, Ali Hamedani, Kai Nordlund +1
We introduce a machine-learning interatomic potential for tungsten using the Gaussian Approximation Potential framework. We specifically focus on properties relevant for simulation…