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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 2: Machine learning atomic interactions with GAPΒΆ

Here are all the tutorials you will need for day 2

  • Getting started
    • Setting up our work environment
  • Preprocessing DFT Data
  • Splitting the trajectory
  • Fitting GAP
  • Using GAP on validation data
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Getting started
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Liquid argon with CP2K
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