Li Bryant Y, Karan Vir, Kaplan Aaron D, Wen Mingjian, Persson Kristin A
Department of Materials Science and Engineering, University of California Berkeley, Berkeley, California 94720, United States.
Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.
J Phys Chem C Nanomater Interfaces. 2025 Sep 3;129(36):16043-16054. doi: 10.1021/acs.jpcc.5c03589. eCollection 2025 Sep 11.
Lithium metal batteries offer superior volumetric and gravimetric specific capacities compared to those based on traditional graphite anodes. Although advancements in solid-state electrolytes address safety concerns, challenges remain, particularly regarding interphase formation in lithium metal anodes. This work presents a computational framework based on high-throughput first-principles density functional theory and machine-learning interatomic potentials (MLIPs) including automated iterative, active learning to enable robust computational exploration of interphase formation between lithium metal anodes and an inorganic solid-state electrolyte. As a demonstration, we apply the framework to a Li/LiPS interface and find that it accurately identifies the experimentally observed, thermodynamically stable interphase products as well as their overall spatial arrangement within a heterogeneous, amorphous layered structure, with LiS domains of nanocrystallinity. Our simulations show two stages, a fast and slow diffusion reaction regime, that corroborate the relative phase formation rate of Li P, LiS, and LiP. Using the Onsager transport theory, we capture time-dependent ionic diffusion within the reacting interface, including cross-correlation effects. We found that cross-correlation effects between Li-P and P-S ionic motion significantly influence P-ion diffusion, making it highly sensitive to the local environment and potentially leading to "kinetic trapping" of Li-P phases. The passivation of the interface is shown as the ionic fluxes all approach zero, effectively halting interphase growth.
与基于传统石墨负极的电池相比，锂金属电池具有更高的体积比容量和重量比容量。尽管固态电解质的进展解决了安全问题，但挑战依然存在，特别是在锂金属负极的界面形成方面。这项工作提出了一个基于高通量第一性原理密度泛函理论和机器学习原子间势（MLIPs）的计算框架，包括自动迭代、主动学习，以实现对锂金属负极与无机固态电解质之间界面形成的稳健计算探索。作为一个示范，我们将该框架应用于Li/LiPS界面，发现它准确地识别了实验观察到的、热力学稳定的界面产物，以及它们在非均质、非晶层状结构内的整体空间排列，其中存在纳米晶的LiS域。我们的模拟显示了两个阶段，一个快速和缓慢扩散反应阶段，这证实了LiP、LiS和LiP的相对相形成速率。使用昂萨格输运理论，我们捕捉了反应界面内随时间变化的离子扩散，包括交叉相关效应。我们发现Li-P和P-S离子运动之间的交叉相关效应显著影响P离子扩散，使其对局部环境高度敏感，并可能导致Li-P相的“动力学捕获”。界面的钝化表现为离子通量都接近零，有效地阻止了界面生长。
