P. Kumar, F. Körmann, B. Grabowski, und Y. Ikeda. Dataset, (2025)Related to: P. Kumar, F. Körmann, B. Grabowski, Y. Ikeda, Machine learning potentials for hydrogen absorption in TiCr2 Laves phases, Acta Materialia (2025) 121319. doi: 10.1016/j.actamat.2025.121319.
Y. Ikeda, und F. Körmann. Dataset, (2025)Related to: Y. Ikeda and F. Körmann, Impact of N on the Stacking Fault Energy and Phase Stability of FCC CrMnFeCoNi: An Ab Initio Study, J. Phase Equilib. Diff. 42, 551 (2021). doi: 10.1007/s11669-021-00877-x.
X. Zhang. Dataset, (2024)Related to: Zhang, X., Divinski, S.V. & Grabowski, B. Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten. Nat Commun 16, 394 (2025). doi: 10.1038/s41467-024-55759-w.
Y. Ou, Y. Ikeda, L. Scholz, S. Divinski, F. Fritzen, und B. Grabowski. Dataset, (2024)Related to: Yongliang Ou, Yuji Ikeda, Lena Scholz, Sergiy Divinski, Felix Fritzen, Blazej Grabowski (2024). Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials. arXiv: 2407.04126.
X. Xu. Dataset, (2024)Related to: Xu, Xiang, Xi Zhang, Erik Bitzek, Siegfried Schmauder, and Blazej Grabowski. "Origin of the yield stress anomaly in L12 intermetallics unveiled with physically-informed machine-learning potentials." Acta Materialia. doi: 10.1016/j.actamat.2024.120423.
P. Srinivasan, D. Demuriya, B. Grabowski, und A. Shapeev. Dataset, (2024)Related to: Srinivasan, P., Demuriya, D., Grabowski, B. et al. Electronic Moment Tensor Potentials include both electronic and vibrational degrees of freedom. npj Comput Mater 10, 41 (2024). doi: 10.1038/s41524-024-01222-9.
