电化学中机器学习原子间势的静电现象学基准：超越能量-力度量
Electrostatic Phenomenology Benchmarks for Machine-Learned Interatomic Potentials in Electrochemistry: Beyond the Energy-Force Metric
AI总结：
针对电化学模拟中MLIP长程相互作用评估的不足，研究人员开发了EPhEct基准套件，通过多类针对性测试补充能量-力度量，实现对MLIP物理准确性的定性诊断。
AI中文摘要：
在电化学模拟中，准确处理机器学习原子间势（MLIP）的长程相互作用至关重要。但仅靠总能量和力的误差不足以确定MLIP的物理准确性，因为无法检测模型的定性不一致性，如镜像电荷吸引、介电屏蔽或电荷转移的预测。我们引入基准套件EPhEct（电化学用静电现象），该套件包含针对性测试用例，用于评估MLIP在电化学相关物理现象上的表现。这些测试涵盖金属电极处的镜像电荷吸引、作为离子与电子屏蔽探针的纵光学声子与横光学声子的分裂、界面水的偶极矩，以及离子放电过程中的费米能级钉扎。这些测试建立了一套定性诊断流程，可作为总能量-力度量的补充。
英文摘要：
Accurate treatment of long-range interactions in machine learning interatomic potentials (MLIPs) is essential for electrochemical simulations. However, aggregate energy and force errors alone are insufficient to establish an MLIP's physical accuracy since they do not detect qualitative inconsistencies in the model such as the prediction of image-charge attraction, dielectric screening, or charge transfer. We introduce a benchmark suite EPhEct (Electrostatic Phenomena for Electrochemistry) of focused test cases designed to evaluate MLIPs on electrochemically relevant physical phenomena. The tests probe for image-charge attraction at a metal electrode, the splitting between longitudinal and transverse optical phonons as a probe of ionic and electronic screening, the dipole moment of interfacial water, and Fermi-level pinning during ion discharge. These tests establish a qualitative diagnostic routine complementary to aggregate energy-force metrics.
