Force field optimization by end-to-end differentiable atomistic simulation

Ähnliche Einträge

• Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters
  von: Yang, Kai, et al.
  Veröffentlicht: (2025)
• Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
  von: Li, Haoyu, et al.
  Veröffentlicht: (2023)
• On phase separation and crystallization of Ge-rich GeSbTe alloys from atomistic simulations with a machine learning interatomic potential
  von: Kheir, Omar Abou El, et al.
  Veröffentlicht: (2026)
• Electronic structure prediction of medium and high entropy alloys across composition space
  von: Pathrudkar, Shashank, et al.
  Veröffentlicht: (2024)
• Multi-Output Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data
  von: Breshears, Madeleine D., et al.
  Veröffentlicht: (2025)