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The Atomistic Cookbook
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The Atomistic Cookbook
  • Recipes grouped by topic
    • Statistical sampling and dynamics
    • Analysis and post-processing
    • Machine learning models
    • Nuclear quantum effects
  • Recipes grouped by software used
    • i-PI
    • chemiscope
    • featomic
    • scikit-matter
    • cp2k
    • LAMMPS
    • metatensor
    • PLUMED
    • torch-pme
  • List of all recipes
    • A ML model for the electron density of states
    • Atomistic Water Model for Molecular Dynamics
    • Batch run of CP2K calculations
    • Constant-temperature MD and thermostats
    • Equivariant linear model for polarizability
    • Equivariant model for tensorial properties based on scalar features
    • Generalized Convex Hull construction for the polymorphs of ROY
    • Learning Capabilities with torchpme
    • Local Prediction Rigidity analysis
    • Long-distance Equivariants: a tutorial
    • MD using direct-force predictions with PET-MAD
    • Multiple time stepping and ring-polymer contraction
    • PCA/PCovR Visualization of a training dataset for a potential
    • Path integral metadynamics
    • Path integral molecular dynamics
    • Periodic Hamiltonian learning
    • Quantum heat capacity of water
    • Rotating equivariants
    • Sample and Feature Selection with FPS and CUR
    • The PET-MAD universal potential
  • Downloading and running the recipes
  • Contributing a recipe
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Recipes grouped by topicΒΆ

You can navigate through the various recipes grouped in thematic areas, including classes of simulation problems and of modeling techniques. Recipes may be listed in more than one area, when relevant.

  • Statistical sampling and dynamics
  • Analysis and post-processing
  • Machine learning models
  • Nuclear quantum effects
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Statistical sampling and dynamics
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