Huang Jianxing, Zhang Linfeng, Wang Han, Zhao Jinbao, Cheng Jun, E Weinan
State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA.
J Chem Phys. 2021 Mar 7;154(9):094703. doi: 10.1063/5.0041849.
Solid-state electrolyte materials with superior lithium ionic conductivities are vital to the next-generation Li-ion batteries. Molecular dynamics could provide atomic scale information to understand the diffusion process of Li-ion in these superionic conductor materials. Here, we implement the deep potential generator to set up an efficient protocol to automatically generate interatomic potentials for LiGePS-type solid-state electrolyte materials (LiGePS, LiSiPS, and LiSnPS). The reliability and accuracy of the fast interatomic potentials are validated. With the potentials, we extend the simulation of the diffusion process to a wide temperature range (300 K-1000 K) and systems with large size (∼1000 atoms). Important technical aspects such as the statistical error and size effect are carefully investigated, and benchmark tests including the effect of density functional, thermal expansion, and configurational disorder are performed. The computed data that consider these factors agree well with the experimental results, and we find that the three structures show different behaviors with respect to configurational disorder. Our work paves the way for further research on computation screening of solid-state electrolyte materials.
具有优异锂离子电导率的固态电解质材料对下一代锂离子电池至关重要。分子动力学可以提供原子尺度信息,以了解锂离子在这些超离子导体材料中的扩散过程。在此,我们采用深度势生成器建立了一种高效方案,用于自动生成LiGePS型固态电解质材料(LiGePS、LiSiPS和LiSnPS)的原子间势。验证了快速原子间势的可靠性和准确性。利用这些势,我们将扩散过程的模拟扩展到较宽的温度范围(300 K - 1000 K)和较大尺寸的体系(约1000个原子)。仔细研究了统计误差和尺寸效应等重要技术方面,并进行了包括密度泛函效应、热膨胀和构型无序在内的基准测试。考虑这些因素的计算数据与实验结果吻合良好,并且我们发现这三种结构在构型无序方面表现出不同的行为。我们的工作为固态电解质材料的计算筛选进一步研究铺平了道路。