Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
This paper introduces a symmetry-restricted benchmark that systematically evaluates the fidelity of universal machine-learned interatomic potentials by comparing their predicted two-dimensional potential energy surface slices against DFT calculations to reveal artifacts and assess their ability to capture critical topological features like local minima and saddle points.