Size and Quality of Quantum Mechanical Data Sets for Training Neural Network Force Fields for Liquid Water

TL;DR

This paper examines how the size and quality of quantum mechanical data sets influence the training of neural network force fields for liquid water, highlighting the importance of sampling strategies for accurate simulations.

Contribution

It investigates the impact of data set size and quality on neural network force fields for liquid water, emphasizing effective sampling over sheer data volume.

Findings

Structural properties are less sensitive to data set size.

Dynamical properties like diffusion coefficient depend more on data quality.

Good sampling enables small data sets to achieve high precision.

Abstract

Molecular dynamics simulations have been used in different scientific fields to investigate a broad range of physical systems. However, the accuracy of calculation is based on the model considered to describe the atomic interactions. In particular, ab initio molecular dynamics (AIMD) has the accuracy of density functional theory (DFT), and thus is limited to small systems and relatively short simulation time. In this scenario, Neural Network Force Fields (NNFF) have an important role, since it provides a way to circumvent these caveats. In this work we investigate NNFF designed at the level of DFT to describe liquid water, focusing on the size and quality of the training data-set considered. We show that structural properties are less dependent on the size of the training data-set compared to dynamical ones (such as the diffusion coefficient), and a good sampling (selecting data reference for training process) can lead to a small sample with good precision.