{\ae}net-PyTorch: a GPU-supported implementation for machine learning atomic potentials training

Understand

In this work, we present {\ae}net-PyTorch, a PyTorch-based implementation for training artificial neural network-based machine learning interatomic potentials.

• Developed as an extension of the atomic energy network ({\ae}net), {\ae}net-PyTorch provides access to all the tools included in {\ae}net for the application and usage of the potentials.
• The package has been designed as an alternative to the internal training capabilities of {\ae}net, leveraging the power of graphic processing units to facilitate direct training on forces in addition to energies.
• This leads to a substantial reduction of the training time by one to two orders of magnitude compared to the CPU implementation, enabling direct training on forces for systems beyond small molecules.