Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations

TL;DR

This paper benchmarks 15 pretrained Machine Learning Interatomic Potentials, evaluating their accuracy, speed, memory, and stability to guide practitioners in selecting suitable models for molecular simulations.

Contribution

It provides an objective comparison of pretrained MLIPs across multiple metrics and insights into factors influencing their performance.

Findings

Number of parameters and training set size correlate with accuracy.

Including Coulomb energy terms offers no accuracy benefit.

Model architecture significantly impacts speed and memory use.

Abstract

The rapid development of pretrained Machine Learning Interatomic Potentials (MLIPs) that cover a wide range of molecular species has made it challenging to select the best model for a given application. We benchmark 15 pretrained MLIPs, evaluating each one on accuracy, speed, memory use, and ability to produce stable simulations. This provides an objective basis for practitioners to select the most appropriate MLIP for their own simulations, and offers insight into which factors most strongly influence model accuracy. We find that the number of model parameters and the size of the training set are both strongly correlated with accuracy, but observe no benefit from including explicit Coulomb energy terms. Speed and memory use are determined as much by the model architecture as by the size of the model.