发表机构
Technische Universität Ilmenau（伊尔梅瑙工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种无参考的机器学习原子间势认证方法，利用Born-Oppenheimer表面的数学物理性质构建十四个廉价探针，对64个预训练势能进行绝对评分，揭示固定测试集无法发现的失败，并标记了一个表面退化导致分子动力学失败的微调模型。
AI 中文摘要
通用机器学习原子间势现在达到的留出能量和力误差如此之小，以至于它们不再能预测模型在模拟中的行为。在这里，我们展示了一个势能可以在没有任何参考计算的情况下，根据精确的Born-Oppenheimer表面在数学或物理上必须满足的性质进行分级。我们将这些性质组织为四个家族：对称性与不变性、自洽性与可积性、统计力学平衡与正则性，并将它们转化为十四个廉价的探针，每个探针都针对一个精确且独立于化学和参考方法的目标进行认证。因此，每个探针都会从势能已经暴露的能量、力和应力中返回一个绝对的、可比较架构的分数，并揭示固定测试集无法发现的失败。应用于64个预训练的势能，该套件在可比的报告精度下解析了两个数量级的认证质量，并标记了一个微调的势能，其参考误差改善而其表面退化直到分子动力学失败。所有64个模型的交互式排行榜可在该https URL获得。
英文摘要
Universal machine-learning interatomic potentials now reach held-out energy and force errors so small that they no longer predict how a model behaves in simulation. Here we show that a potential can be graded without any reference calculation, against properties the exact Born-Oppenheimer surface satisfies by mathematical or physical necessity. We organise these properties into four families, symmetry and invariance, self-consistency and integrability, statistical-mechanical equilibrium and regularity, and turn them into fourteen inexpensive probes, each certifying against a target that is exact and independent of chemistry and reference method. Every probe therefore returns an absolute, architecture-comparable score from the energies, forces and stresses a potential already exposes, and reveals failures a fixed test set cannot. Applied to 64 pretrained potentials, the suite resolves two orders of magnitude of certified quality at comparable reported accuracy, and flags a fine-tuned potential whose reference errors improve while its surface degrades until molecular dynamics fails. An interactive leaderboard of all 64 models is available at https://jhaens.github.io/pescert-bench.
