Revealing the Staging Structural Evolution and Li (De)Intercalation Kinetics in Graphite Anodes via Machine Learning Potential

Documenti analoghi

• High-Throughput NEB for Li-Ion Conductor Discovery via Fine-Tuned CHGNet Potential
  di: Lian, Jingchen, et al.
  Pubblicazione: (2025)
• Influence of Exchange-Correlation Functionals and Neural Network Architectures on Li$^+$-Ion Conductivity in Solid-State Electrolyte from Molecular Dynamics Simulations with Machine-Learning Force Fields
  di: Li, Zicun, et al.
  Pubblicazione: (2025)
• EDIS: A Simulation Software for Dynamic Ion Intercalation/Deintercalation Processes in Electrode Materials
  di: Wang, Liqi, et al.
  Pubblicazione: (2025)
• High-Entropy Solid Electrolytes Discovery: A Dual-Stage Machine Learning Framework Bridging Atomic Configurations and Ionic Transport Properties
  di: Fu, Xiao, et al.
  Pubblicazione: (2025)
• Li$_{14}$Mn$_{2}$S$_{9}$ and Li$_{10}$Si$_{2}$S$_{9}$ as a pair of all-electrochem-active electrode and solid-state electrolyte with chemical compatibility and low interface resistance
  di: Yang, Qifan, et al.
  Pubblicazione: (2024)