Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces

TL;DR

This paper evaluates the ability of universal machine learning interatomic potentials to predict surface energies, revealing current limitations and emphasizing the need for broader training datasets to improve their generalization to surfaces.

Contribution

It provides a systematic assessment of existing universal MLIPs' performance on surface energy calculations, highlighting their shortcomings and suggesting directions for expanding training datasets.

Findings

Universal MLIPs show significant errors in surface energy predictions.

Errors correlate with the total energy and out-of-domain data.

Universal MLIPs are useful for fine-tuning but need broader datasets.

Abstract

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and complex interactions. A recent class of advanced MLIPs, which use equivariant representations and deep graph neural networks, is known as universal models. These models are proposed as foundational models suitable for any system, covering most elements from the periodic table. Current universal MLIPs (UIPs) have been trained with the largest consistent dataset available nowadays. However, these are composed mostly of bulk materials' DFT calculations. In this article, we assess the universality of all openly available UIPs, namely MACE, CHGNet, and M3GNet, in a representative task of generalization: calculation of surface energies. We find that the out-of-the-box foundational models have significant shortcomings in this task, with errors correlated to the total energy of surface simulations, having an out-of-domain distance from the training dataset. Our results show that while UIPs are an efficient starting point for fine-tuning specialized models, we envision the potential of increasing the coverage of the materials space towards universal training datasets for MLIPs.