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metatrain 0.1.dev1+gd79b709bc documentation
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  • Installation
  • Getting started
    • Quickstart
    • Basic Usage
    • Customize a Dataset Configuration
    • Advanced Base Configuration
    • Override Architecture’s Default Parameters
    • Restarting and Checkpoints
    • Finetuning example
  • Configuration and Units
    • Units
  • Available Architectures
    • GAP
    • LLPR
    • NanoPET (deprecated)
    • PET
    • SOAP-BPNN
  • Tutorials
    • Beginner Tutorials
      • How to prepare data for training
      • Fine-tuning
      • Training a model from scratch
      • Running molecular dynamics with ASE
    • Advanced Tutorials
      • Transfer Learning (experimental)
      • Computing LLPR uncertainties
      • Training a model with ZBL corrections
      • Fitting generic targets
  • Concepts and Design
    • Output naming
    • Loss functions
    • Auxiliary outputs
    • Multi-GPU training
  • 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
        • Composition model
        • ZBL short-range potential
      • Data
        • Combining dataloaders
        • Dataset
        • Reading a dataset
        • Readers
        • Target data Writers
        • Converting Systems to ASE
      • Architectures
      • Augmentation
      • Device
      • Dtype
      • Errors
      • Evaluating a model
      • External Naming
      • IO
      • Jsonschema
      • Logging
      • Long-range
      • Loss
      • Metrics
      • Neighbor lists
      • Custom omegaconf functions
      • Output gradient
      • Averaging predictions per atom
      • Scaler
      • Summing over atoms
      • Data type and device transfers
      • Unit handling
    • Changelog
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Advanced Tutorials¶

This sections includes the advanced tutorials on the usage of the metatrain package.

  • Transfer Learning (experimental)
  • Computing LLPR uncertainties
  • Training a model with ZBL corrections
  • Fitting generic targets
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Transfer Learning (experimental)
Previous
Running molecular dynamics with ASE
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