The ability to predict materials properties from atomistic simulations is essential for modern materials design. Machine learning interatomic potentials (MLIPs), trained on data from electronic ...

Machine learning interatomic potentials, as a modern generation of classical force fields, take atomic environments as input and predict the corresponding atomic energies and forces. We challenge the ...

illustrating the comprehensive zero-shot benchmark of 19 universal machine learning interatomic potentials and the dominant impact of training data composition for surface energy prediction. A ...