Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning

Ähnliche Einträge

• Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential
  von: Leimeroth, Niklas, et al.
  Veröffentlicht: (2024)
• Machine-learning interatomic potentials from a users perspective: A comparison of accuracy, speed and data efficiency
  von: Leimeroth, Niklas, et al.
  Veröffentlicht: (2025)
• Understanding phase transitions of $α$-quartz under dynamic compression conditions by machine-learning driven atomistic simulations
  von: Erhard, Linus C., et al.
  Veröffentlicht: (2024)
• Chemo-mechanical coupling stabilizes mixed $\mathrm{Ag}_{x}\mathrm{Cu}_{1-x}\mathrm{GaSe}_{2}$ solar-cell absorbers: Insights from Monte-Carlo simulations assisted by ab initio informed machine-learning potentials
  von: Karanikolas, Vasilios, et al.
  Veröffentlicht: (2026)
• Electron Polaron at Neutral 180$^\circ$ Domain Wall in PbTiO$_3$: Stability, Trapping Energies, and Transverse Polarization
  von: Amirabbasi, Mohammad, et al.
  Veröffentlicht: (2026)