Development of machine-learned interatomic potentials to predict structure, transport, and reactivity in platinum-based fuel cells

Documenti analoghi

• Improving the reliability of machine learned potentials for modeling inhomogeneous liquids
  di: Kamron Fazel, et al.
  Pubblicazione: (2024)
• Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
  di: Karimitari, Nima, et al.
  Pubblicazione: (2026)
• Bridging electronic and classical density-functional theory using universal machine-learned functional approximations
  di: Kelley, Michelle M., et al.
  Pubblicazione: (2024)
• Static Subspace Approximation for Random Phase Approximation Correlation Energies: Implementation and Performance
  di: Weinberg, Daniel, et al.
  Pubblicazione: (2024)
• Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
  di: Stark, Wojciech G., et al.
  Pubblicazione: (2024)