  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.
