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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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Fitting¶

graph-pes provides a number of utilities for fitting models to data. These are used internally by graph-pes-train.

Contents¶

  • Losses
    • Losses
    • Metrics
    • Helpers
  • Optimizers
    • Optimizer
    • Schedulers
  • Callbacks
    • WandbLogger
    • OffsetLogger
    • ScalesLogger
    • DumpModel
    • ModelTimer
    • Base class
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