发表机构
Department of Materials Science and Engineering, University of Maryland（马里兰大学材料科学与工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FPBench提出面向应用的基准，通过误差分解指标评估基础势在力预测、能量排序和离子迁移等任务中的实际可靠性，揭示平均误差无法预测任务性能，并为模型开发提供针对性指导。
AI 中文摘要
基础势（FPs）已成为原子建模的新基础。虽然使用平均能量和力误差的评估通常表明其接近DFT精度，但它们在实用计算研究中的表现仍然不一致。在此，我们提出FPBench，一个面向应用的基准测试，用于评估基础势在代表性计算任务上的表现。多个最先进的基础势在三个基本任务上进行了评估：原子模拟的力预测、替代和空位有序的能量排序，以及离子/空位迁移。对这些基础势的基准测试表明，平均力和能量误差往往无法预测任务性能，揭示了实际可靠性上的显著差异。FPBench通过根据物理上重要的量和配置（这些量和配置决定计算任务）来解析性能的指标，引入了面向应用的误差分解，包括高精度原子和大力误差原子的比例、远离平衡的原子、相对相稳定性和凸包一致性，以及离子迁移中的路径误差。这些误差分解指标确定了特定计算任务中FP误差出现的位置，为模型开发提供了有针对性的指导。FPBench提供了一个开放基准、评估代码和一个公共排行榜，用于严格的基础势评估和开发。
英文摘要
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.
