FP-DeErr: Application-Oriented Error Decomposition for Foundation Potentials
Foundation potentials (FPs) have emerged as a new basis for atomistic modeling. While their evaluation using average energy and force errors often indicates near-DFT accuracy, their performance in practical computational studies remains inconsistent. While recent benchmarks evaluate FPs on downstream computational tasks, the underlying errors that determine task success or failure are not always clear. Here, we develop FP-DeErr, an application-oriented error-decomposition framework that resolves FP errors according to the physically meaningful quantities, configurations, and computational stages governing specific tasks. Rather than averaging errors over an entire dataset, FP-DeErr uses decomposed error metrics devised for physically meaningful quantities, focusing on the configurations where errors arise and matter most. We demonstrate FP-DeErr by evaluating multiple state-of-the-art FPs on three fundamental tasks: force prediction for atomistic simulations, energy ranking for substitutional and vacancy orderings, and ion/vacancy migration. These error-decomposition metrics resolve force errors among highly accurate, large-error, and far-from-equilibrium atoms; relative-energy errors among competing orderings, phases, and compositions; and ion migration errors among endpoints and along-path errors. By identifying where FP errors arise, this error decomposition provides targeted guidance for FP development. FP-DeErr also provides an open benchmark, evaluation code, and a public leaderboard for rigorous FP assessment.