短程机器学习互原子势中的虚假金属化
False Metallization in Short-Ranged Machine Learned Interatomic Potentials
AI总结：
本文揭示短程MLIPs因忽略长程静电相互作用导致水层虚假金属化的问题，强调长程静电相互作用对研究极性液体系统的重要性。
AI中文摘要：
机器学习互原子势（MLIPs）已使原子模拟在接近从头计算精度的情况下实现了计算成本的大幅降低。然而，许多广泛使用的MLIPs是短程的，无法准确捕捉长程静电相互作用。在与极性溶剂如水的界面处，这种缺陷可能导致远离界面的非物理长距离偶极子对齐。本文揭示了忽略长程物理导致水层中虚假金属化的原因，这是由于总溶剂偶极子的异常大波动所引起，类似于在极性界面处观察到的防止极性灾难的电子重新排列。这种金属化在显式包含长程静电相互作用的MLIPs中被消除。我们的结果展示了短程MLIPs的根本缺陷，强调长程静电相互作用对于研究包含极性液体组分的系统至关重要，特别是当研究者关注电子性质时。
英文摘要：
Machine learned interatomic potentials (MLIPs) have enabled atomistic simulations with ab initio accuracy for a fraction of the computational cost. However, many widely used MLIPs are short-ranged and do not accurately capture long-ranged electrostatic interactions. At interfaces with polar solvents, such as water, this deficiency can drive unphysical long-distance dipolar alignment far away from the interface. Here we reveal that neglecting long-ranged physics leads to spurious metallization of the water layer due to artificially large fluctuations of the total solvent dipole, similar to the electron rearrangement observed to prevent polar catastrophes at polar interfaces. This metallization is eliminated in MLIPs that explicitly include long-ranged electrostatics. Our results showcase a fundamental flaw of short-ranged MLIPs, highlighting that long-ranged electrostatics are essential for studying systems with a polar-liquid component, especially if one is interested in electronic properties.
