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

Abstract

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 CPU 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 data set is not necessary, and including between 10% to 20% of the force information is sufficient to achieve optimally accurate interatomic potentials with the least computational resources.

9 pages, 7 figures and 1 table

References in corpus (7)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• ADADELTA: An Adaptive Learning Rate Method
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• Machine Learning Unifies the Modelling of Materials and Molecules
• On-the-fly machine learning force field generation: Application to melting points
• Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
• AENET-LAMMPS and AENET-TINKER: Interfaces for Accurate and Efficient Molecular Dynamics Simulations with Machine Learning Potentials