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graph-pes
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  • Quickstart
    • Train a model
    • Model analysis
    • Fine-tuning
    • Implement a model
    • Custom training loops

CLI Reference

  • graph-pes-train
    • The basics
    • Config options
    • Example configs
  • graph-pes-resume
  • graph-pes-test
  • graph-pes-id

API Reference

  • Data
    • Atomic Graphs
    • Datasets
    • Loader
  • Models
    • Addition Model
    • Energy Offset
    • Pair Potentials
    • Many Body Models
      • SchNet
      • PaiNN
      • NequIP
      • MACE
      • TensorNet
  • Fitting
    • Losses
    • Optimizers
    • Callbacks
  • Building blocks
    • Distance Expansions
    • Envelopes
    • Aggregation
    • Scaling
    • PyTorch Helpers
    • e3nn Helpers
  • Utils

Interfaces

  • mace-torch
  • mattersim

Tools

  • ASE
  • LAMMPS
  • Analysis

About

  • Theory
  • Development
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Many Body ModelsΒΆ

graph-pes has re-implemented the following, popular many-bodied, machine-learned interatomic potentials:

  • SchNet
  • PaiNN
  • NequIP
  • MACE
  • TensorNet
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SchNet
Previous
Pair Potentials
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