About

Academy research fellow (Docent, PhD) in computational materials physics at University of Helsinki, Department of Physics.

My main research interest is development of machine-learning and analytical interatomic potentials for molecular dynamics simulations. I also use them to simulate radiation damage in various materials. Current research topics:

• Machine-learning potentials for metals, alloys, and semiconductors.

• tabGAP: Tabulated Gaussian approximation potentials for high-entropy alloys.

• Plasticity of (high-entropy) alloys.

• Radiation damage in metals, alloys, and semiconductors.

Selected/highlighted publications

J. Byggmästar, D. Sobieraj, J. S. Wróbel, D. T. Schreiber, O. El-Atwani, E. Martinez, D. Nguyen-Manh, Segregation, ordering, and precipitation in WTaV-based concentrated refractory alloys, Acta Materialia, 296 121276 (2025) https://arxiv.org/abs/2412.13750, https://doi.org/10.1016/j.actamat.2025.121276

G.Y. Wei, J. Byggmästar, J. Cui, K. Nordlund, J. Ren, F. Djurabekova, Revealing the critical role of vanadium in radiation damage of tungsten-based alloys, Acta Materialia, 274 119991 (2024), https://doi.org/10.1016/j.actamat.2024.119991

M.A. Tunes, D. Parkison, B. Sun, P. Willenshofer, S. Samberger, C. Fruhwirth, S. Tripathi, B.K. Derby, J.K.S. Baldwin, S.J. Fensin, D. Sobieraj, J.S. Wróbel, J. Byggmästar, S. Pogatscher, E. Martinez, D. Nguyen-Manh, O. El-Atwani, High Radiation Resistance in the Binary W-Ta System Through Small V Additions: A New Paradigm for Nuclear Fusion Materials, Advanced Science, 12, 20, 2417659 (2025), https://arxiv.org/abs/2406.15022, https://doi.org/10.1002/advs.202417659

J. Byggmästar, K. Nordlund, and F. Djurabekova, Modelling refractory high-entropy alloys with machine-learned interatomic potentials: Defects and segregation, Physical Review B, 104, 104101 (2021), https://arxiv.org/abs/2106.03369, https://doi.org/10.1103/PhysRevB.104.104101

J. Byggmästar, K. Nordlund, and F. Djurabekova, Simple machine-learned interatomic potentials for complex alloys, Physical Review Materials, 6, 083801 (2022), https://arxiv.org/abs/2203.08458, https://doi.org/10.1103/PhysRevMaterials.6.083801

J. Byggmästar, A. Hamedani, K. Nordlund, and F. Djurabekova, Machine-learning interatomic potential for radiation damage and defects in tungsten, Physical Review B, 100, 144105 (2019), https://arxiv.org/abs/1908.07330, https://doi.org/10.1103/PhysRevB.100.144105

Contact information

email: jesper.byggmastar(a)helsinki.fi