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Machine Learning of Atomic Interactions documentation
Machine Learning of Atomic Interactions documentation

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
  • Examples
    • Calculating the radial distribution function
    • Fitting GAP
    • Simulation of Argon
    • Calculating the 2 body potential of Argon using ASE and CP2K
    • Using GAP on validation data
    • Preprocessing DFT Data
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Day 1: Quantum mechanical calculations with DFTΒΆ

Here are all the tutorials you will need for day 1

  • Getting started
    • Setting up our work environment
  • Calculating the 2 body potential of Argon using ASE and CP2K
  • Liquid argon with CP2K
    • Getting started
    • Force Calculation with DFT
      • System definition
    • Molecular dynamics
      • Writing coordinates and forces
    • Running your simulation
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