ænet-python
  • Installation & Set-up
  • Choosing an Implementation
  • Structure conversion and manipulation
  • Basic Structure Transformations
  • Advanced Structure Transformations
  • Data acquisition with tools/sconv
  • Command-line tools
  • Structure featurization
  • Training ANN Potentials (Fortran)
  • Using ANN Potentials with ænet’s Fortran Binaries
  • ænet training set files
  • PyTorch-Based Featurization
  • PyTorch-based Training
  • PyTorch Dataset Options
  • Using ANN Potentials with the PyTorch Implementation
  • Neighbor Lists
  • Defining Command-line tools
  • Documentation Example Testing
  • Analytical Gradients for Chebyshev AUC Descriptors
  • Unified HDF5 Torch Cache Schema
  • API Reference
ænet-python
  • Overview: module code

All modules for which code is available

  • aenet.commandline.aenet_config
  • aenet.commandline.aenet_sconv
  • aenet.commandline.aenet_sfp
  • aenet.commandline.tools
  • aenet.geometry.transformations.atomic
  • aenet.geometry.transformations.base
  • aenet.geometry.transformations.cell
  • aenet.geometry.transformations.strain
  • aenet.reference_energies
  • aenet.torch_featurize.chebyshev
  • aenet.torch_featurize.featurize
  • aenet.torch_nblist.neighborlist
  • aenet.torch_training.builders.network_builder
  • aenet.torch_training.builders.optimizer_builder
  • aenet.torch_training.committee
  • aenet.torch_training.inference.predictor
  • aenet.torch_training.training.checkpoint_manager
  • aenet.torch_training.training.metrics
  • aenet.torch_training.training.normalization
  • aenet.torch_training.training.training_loop
  • aenet.trainset

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