Machine Learning of Atomic Interactions Tutorials

Contents:

• Day 1: Quantum mechanical calculations with DFT
  • Getting started
  • Calculating the 2 body potential of Argon using ASE and CP2K
  • Liquid argon with CP2K
• Day 2: Machine learning atomic interactions with GAP
  • Getting started
  • Preprocessing DFT Data
  • Splitting the trajectory
  • Fitting GAP
  • Using GAP on validation data
• Day 3: Molecular dynamics simulations with ML potentials
  • Molecular Dynamics with LAMMPS interfaced to QUIP
  • Calculating the radial distribution function
  • MD of liquid argon at 95K
• Examples

Note: The tutorials are still a work in progress and incomplete.