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metatrain 0.1.dev1+g086f04abc documentation
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  • Installation
  • Getting started
    • Quickstart
    • Training YAML Reference
    • Override Architecture’s Default Parameters
    • Restarting and Checkpoints
    • Finetuning example
    • Units
  • Available Architectures
    • FlashMD
    • GAP
    • LLPR
    • NanoPET (deprecated)
    • PET
    • SOAP-BPNN
  • Tutorials
    • Beginner Tutorials
      • Basic Usage
      • How to prepare data for training
      • Fine-tune 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 a FlashMD model
      • Multi-GPU training
      • Training with Mixed Stress Structures
      • Training a DOS model
      • Generating and training an LLPR-derived shallow ensemble model
  • Concepts and Design
    • Output naming
    • Loss functions
    • Auxiliary outputs
  • 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
        • Composition model
        • 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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Beginner Tutorials¶

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

Basic Usage

Basic Usage

How to prepare data for training

How to prepare data for training

Fine-tune a pre-trained model

Fine-tune a pre-trained model

Training a model from scratch

Training a model from scratch

Model validation with parity plots

Model validation with parity plots

Running molecular dynamics with ASE

Running molecular dynamics with ASE
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Basic Usage
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Tutorials
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