ænet-PyTorch: A GPU-supported implementation for machine learning atomic potentials training

Jon López-Zorrilla, Xabier M. Aretxabaleta, In Won Yeu, Iñigo Etxebarria, Hegoi Manzano, Nongnuch Artrith*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


In this work, we present ænet-PyTorch, a PyTorch-based implementation for training artificial neural network-based machine learning interatomic potentials. Developed as an extension of the atomic energy network (ænet), ænet-PyTorch provides access to all the tools included in ænet for the application and usage of the potentials. The package has been designed as an alternative to the internal training capabilities of ænet, 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 central processing unit implementation, enabling direct training on forces for systems beyond small molecules. Here, we demonstrate the main features of ænet-PyTorch and show its performance on open databases. Our results show that training on all the force information within a dataset is not necessary, and including between 10% and 20% of the force information is sufficient to achieve optimally accurate interatomic potentials with the least computational resources.

Original languageEnglish
Article number164105
Pages (from-to)1-10
Number of pages10
JournalJournal of Chemical Physics
Issue number16
Publication statusPublished - 28 Apr 2023


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