# A Performance and Cost Assessment of Machine Learning Interatomic Potentials

## TL;DR

This paper evaluates various machine learning-based interatomic potentials across different materials, demonstrating their superior accuracy over classical models and discussing the trade-offs between accuracy and computational cost.

## Contribution

It provides a comprehensive comparison of four ML-IAP descriptors using diverse DFT data, highlighting their performance and trade-offs for molecular dynamics applications.

## Key findings

- ML-IAPs outperform classical potentials in energy and force predictions
- All studied descriptors accurately predict elastic and phonon properties
- Trade-offs exist between model accuracy and computational cost

## Abstract

Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of ML-IAPs based on four local environment descriptors --- Behler-Parrinello symmetry functions, smooth overlap of atomic positions (SOAP), the Spectral Neighbor Analysis Potential (SNAP) bispectrum components, and moment tensors --- using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model, and consequently computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.