Evaluating and improving the predictive accuracy of mixing enthalpies and volumes in disordered alloys from universal pre-trained machine learning potentials

TL;DR

This study evaluates the accuracy of universal pre-trained machine learning potentials in predicting mixing enthalpies and volumes in disordered alloys, highlighting their limitations and potential improvements for materials science applications.

Contribution

It provides a critical assessment of UPMLIPs' performance in alloy property predictions and demonstrates how targeted training data can enhance their accuracy.

Findings

UPMLIPs struggle to accurately predict mixing energies in alloys.

Supplementing training data improves the potentials' performance.

UPMLIPs can partially accelerate calculations by replacing structural relaxation.

Abstract

The advent of machine learning in materials science opens the way for exciting and ambitious simulations of large systems and long time scales with the accuracy of ab-initio calculations. Recently, several pre-trained universal machine learned interatomic potentials (UPMLIPs) have been published, i.e., potentials distributed with a single set of weights trained to target systems across a very wide range of chemistries and atomic arrangements. These potentials raise the hope of reducing the computational cost and methodological complexity of performing simulations compared to models that require for-purpose training. However, the application of these models needs critical evaluation to assess their usability across material types and properties. In this work, we investigate the application of the following UPMLIPs: MACE, CHGNET, and M3GNET to the context of alloy theory. We calculate the mixing enthalpies and volumes of 21 binary alloy systems and compare the results with DFT calculations to assess the performance of these potentials over different properties and types of materials. We find that small relative energies necessary to correctly predict mixing energies are generally not reproduced by these methods with sufficient accuracy to describe correct mixing behaviors. However, the performance can be significantly improved by supplementing the training data with relevant training data. The potentials can also be used to partially accelerate these calculations by replacing the ab-initio structural relaxation step.