  Machine-learning interatomic potentials have revolutionized materials
modeling at the atomic scale. Thanks to these, it is now indeed possible to
perform simulations of \abinitio quality over very large time and length
scales. More recently, various universal machine-learning models have been
proposed as an out-of-box approach avoiding the need to train and validate
specific potentials for each particular material of interest. In this paper, we
review and evaluate five different universal machine-learning interatomic
potentials (uMLIPs), all based on graph neural network architectures which have
demonstrated transferability from one chemical system to another. The
evaluation procedure relies on data both from a recent verification study of
density-functional-theory implementations and from the Materials Project.
Through this comprehensive evaluation, we aim to provide guidance to materials
scientists in selecting suitable models for their specific research problems,
offer recommendations for model selection and optimization, and stimulate
discussion on potential areas for improvement in current machine-learning
methodologies in materials science.
