Predicting structure-dependent Hubbard U parameters for assessing hybrid functional-level exchange via machine learning

Documenti analoghi

• GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials
  di: Xie, Fankai, et al.
  Pubblicazione: (2024)
• BMach: a Bayesian machine for optimizing Hubbard U parameters in DFT+U with machine learning
  di: Das, Ritwik
  Pubblicazione: (2024)
• Real-time time-dependent density functional theory simulations with range-separated hybrid functionals for periodic systems
  di: Ji, Yuyang, et al.
  Pubblicazione: (2025)
• Efficient hybrid-functional-based force and stress calculations for periodic systems with thousands of atoms
  di: Lin, Peize, et al.
  Pubblicazione: (2024)
• Intelligent structural design of composite concrete‐encased steel columns based on hybrid machine learning and multiobjective optimization
  di: Yuzhuo Zhang, et al.
  Pubblicazione: (2025)