Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: a case study of silicon nitride

Documenti analoghi

• Vacancy-concentration-dependent thermal stability of fcc-(Ti,Al)Nx predicted via chemical-environment-sensitive diffusion activation energies
  di: Nayak, Ganesh Kumar, et al.
  Pubblicazione: (2025)
• Heterogeneous integration of silicon nitride and amorphous silicon carbide photonics
  di: Li, Zizheng, et al.
  Pubblicazione: (2025)
• Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential
  di: Rajabpour, Ali, et al.
  Pubblicazione: (2024)
• Structure and mechanical properties of monolayer amorphous carbon and boron nitride
  di: Zhang, Xi, et al.
  Pubblicazione: (2023)
• Cryogenic microwave performance of silicon nitride and amorphous silicon deposited using low-temperature ICPCVD
  di: Sun, Jiamin, et al.
  Pubblicazione: (2024)