ænet-python Documentation

The aenet-python package is a collection of utilities for preparing input files for, processing output files for, and generally interacting with the machine-learning interatomic potential (MLIP) software ænet.

Common use cases of the package are

• Extraction of structures, energies, and forces from the output of first-principles calculations;

• Interconversion of atomic structure formats, especially conversion to ænet’s XSF format;

• Manipulation of atomic structures, e.g., for generating reference data;

• Operations on the featurized reference data produced by ænet’s generate.x;

• Training of machine-learning interatomic potentials using ænet’s train.x;

• Predicting structural energies and interatomic forces using ænet’s predict.x;

• Analysis of the outputs generated by ænet’s train.x or predict.x; and

• Using a PyTorch-based implementation of ænet’s featurization and training algorithms for GPU-accelerated MLIP training and inference.

Some of the package’s functionality is exposed through command-line tools. Specifically, the tool sconv (structure conversion) makes available capabilities for atomic structure modification and interconversion and sfp (structure fingerprints) can be used to featurize atomic structures. In addition, the config tool can be used for configuration.

See Command-line tools for an overview of the command-line capabilities.