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Metatrain
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  • Installation
  • Getting started
    • Quickstart
    • Training YAML Reference
    • Override Architecture’s Default Parameters
    • Restarting and Checkpoints
    • Units
  • Available Architectures
    • Classifier (Experimental)
    • Composition
    • DPA3 (Experimental)
    • FlashMD (Experimental)
    • Symplectic FlashMD (Experimental)
    • GAP
    • LLPR
    • MACE (Experimental)
    • PET
    • SOAP-BPNN
    • SPACE (Experimental)
  • Available Hooks
    • Global multipoles
    • Identity
    • Intensive gap
    • Tensor basis
  • Tutorials
    • Beginner Tutorials
      • Basic Usage
      • How to prepare data for training
      • Fine-tuning a pre-trained model
      • Training a model from scratch
      • Model validation with parity plots
      • Running molecular dynamics with ASE
    • Advanced Tutorials
      • Transfer Learning (experimental)
      • Computing LLPR uncertainties
      • Training a model with ZBL corrections
      • Fitting generic targets
      • Training or fine-tuning a FlashMD model
      • Multi-GPU training
      • Training with Mixed Stress Structures
      • Training a DOS model
      • Generating and training an LLPR-derived shallow ensemble model
      • Training a Classifier Model
      • Training a Symplectic FlashMD Model
      • Extracting Hidden Layer Features from PET
  • Concepts and Design
    • Dataset formats
    • Output naming
    • Fine-tune a pre-trained model
    • Loss functions
    • Using hooks
    • Target scaling
    • Auxiliary outputs
    • Training with atom-count-based batching
  • Frequently Asked Questions
  • Citing metatrain
  • Developer documentation
    • Contributing
    • Life Cycle of an Architecture
    • Adding a new architecture
    • Dataset Information
    • Adding a new loss function
    • CLI API
      • Train
      • Eval
      • Export
      • Formatter
    • Utility API
      • Additive models
        • Removing additive contributions
        • ZBL short-range potential
      • Scaler models
        • Scaler model
        • Removing the scale from targets
      • Data
        • Combining dataloaders
        • Dataset
        • Reading a dataset
        • Readers
        • Target data Writers
        • Converting Systems to ASE
      • Abstract base classes
      • Architectures
      • Augmentation
      • Device
      • Dtype
      • Errors
      • Evaluating a model
      • External Naming
      • Hyperparameters
      • IO
      • Logging
      • Long-range
      • Loss
      • Metrics
      • Neighbor lists
      • Custom omegaconf functions
      • Output gradient
      • Averaging predictions per atom
      • Pydantic utilities
      • Summing over atoms
      • Testing Utilities
      • Data type and device transfers
      • Unit handling
    • Base hyperparameters
    • Changelog
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Available Hooks¶

This is a list of all hooks available in metatrain. The concept of hooks is explained in Using hooks.

  • Global multipoles
  • Identity
  • Intensive gap
  • Tensor basis
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Global multipoles
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SPACE (Experimental)
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