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Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian

Viktor Zaverkin¹, David Holzmüller², Ingo Steinwart²

¹Institute for Theoretical Chemistry, University of Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany.

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|September 29, 2021

Summary

This study introduces an improved neural network (NN) architecture for faster training of interatomic potentials. The enhanced model boosts prediction accuracy and efficiency for large-scale atomistic simulations, benefiting active learning workflows.

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Area of Science:

Computational chemistry
Materials science
Machine learning

Background:

Artificial neural networks (NNs) are key for interatomic potentials in atomistic simulations.
Training NNs on energies and forces for molecular dynamics is computationally intensive.
Existing Gaussian moment-based neural network (GM-NN) models offer near ab initio accuracy.

Purpose of the Study:

To develop an improved NN architecture for faster and more accurate interatomic potential training.
To extend the applicability of Gaussian moment-based potentials to periodic systems.
To enhance the robustness and transferability of machine learning potentials.

Main Methods:

An improved neural network architecture based on the GM-NN model was developed.
The methodology was extended to handle periodic systems.
Simulations were performed to evaluate prediction accuracy, training time, and model transferability.

Main Results:

The improved NN architecture demonstrated enhanced prediction accuracy.
Training times were considerably reduced compared to previous models.
The models showed excellent transferability and robustness, especially for periodic systems.

Conclusions:

The enhanced NN methodology significantly accelerates the training of interatomic potentials.
This advancement is crucial for computationally demanding workflows like active learning and on-the-fly learning.
The improved models are robust and transferable for both non-periodic and periodic systems.