Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction accuracy. However, a challenge in their application to ionic systems is the treatment of long-ranged electrostatics. Here, we present a highly accurate electrostatic Spectral Neighbor Analysis Potential (eSNAP) for ionic α-Li₃N, a prototypical lithium superionic conductor of interest as a solid electrolyte or coating for rechargeable lithium-ion batteries. We show that the optimized eSNAP model substantially outperforms traditional Coulomb–Buckingham potential in the prediction of energies and forces, as well as various properties, such as lattice constants, elastic constants, and phonon dispersion curves. We also demonstrate the application of eSNAP in long-time, large-scale Li diffusion studies in Li₃N, providing atomistic insights into measures of concerted ionic motion (e.g., the Haven ratio) and grain boundary diffusion. This work aims at providing an approach to developing quantum-accurate force fields for multi-component ionic systems under the SNAP formalism, enabling large-scale atomistic simulations for such systems.

基于局部环境描述符的机器学习的原子间势代表了基于刚性功能形式的传统势在预测准确性方面的传统飞跃。然而，将其应用到离子系统中的挑战是远程静电的处理。在这里，我们提出了一个高度准确的静电谱邻元素分析电位（eSNAP）为离子α -Li ₃N，一种感兴趣的原型锂离子超导体，作为固体电解质或可充电锂离子电池的涂层。我们显示，优化的eSNAP模型在预测能量和力以及各种特性（例如晶格常数，弹性常数和声子色散曲线）方面，其性能远胜于传统的库仑-白金汉势。我们还证明了eSNAP在Li ₃ N中长期，大规模的Li扩散研究中的应用，提供了对离子协同运动（例如，Haven比率）和晶界扩散的测量的原子见解。这项工作旨在提供一种在SNAP形式主义下为多组分离子系统开发量子精确力场的方法，从而实现此类系统的大规模原子模拟。