HomeCasesLi diffusion in Li10GeP2S12 sulfide solid electrolyte

Li diffusion in Li10GeP2S12 sulfide solid electrolyte

Overview

Ion-conducting solid electrolytes have been known for a long time, but have undergone remarkable development in the past decade or so. In particular, sulfide-based solid electrolytes have dramatically improved their ionic conductivity and are expected to be used in all-solid-state lithium-ion batteries.

Li10GeP2S12 (LGPS) belongs to the category that shows the highest Li-ion conductivity among the existing sulfide solid electrolytes, and its unique crystal structure contributes to the high performance.

In this section, we compute the diffusion coefficient of Li-ion in LGPS by molecular dynamics calculation using Matlantis.

The sample script is uploaded on github:

image

The model and method

In this section, we will optimize the crystal structure of LGPS and calculate the diffusion coefficient of Li.

The crystal structure of LGPS can be obtained from the original paper [1] or Materials Project [2]. Based on the obtained structure, we will analyze the behavior of lithium ion by molecular dynamics after confirming the reproducibility by structural optimization.

We adopt NVT ensemble using Langevin dynamics simulation.

image

Results and discussion

image
image

It is known that in LGPS, diffusion in the c-axis (=z direction) is most prominent and limited in the perpendicular directions. The MSD plot clearly shows such feature. The diffusion coefficients can be computed from the MSD and shown in the Arrhenius plot.

The activation energy is found to be in good agreement with the DFT values as well as the experimental value reported in the literature.[3-4]

This type of MD simulations are often performed at very high temperatures when a conventional DFT is used. However, it can be performed at much lower temperatures using Matlantis because of the low computational cost, and the results can be more readily compared with the experimental data.

Calculation Condition

image

References

[1] N. Kamaya, et. al., Nature Mater 10, 682–686 (2011). https://www.nature.com/articles/nmat3066 [2] https://materialsproject.org/ [3] Mo et al. Chem.Mater. (2012) 24, 15-17 https://pubs.acs.org/doi/10.1021/cm203303y [4] Y. Kato, et. al. Nat. Energy 1, 16030. https://www.nature.com/articles/nenergy201630
Contact Us
Go to List of Cases
Features
Features

Matlantis: 3 Key Features

Matlantis supports companies exploring innovative materials for a sustainable future.

Versatility

汎用性 / Versatile イメージ
Supports a wide range of elements and structures

Matlantis now supports 96 elements from the periodic table, covering all elements occurring in nature. This means users will encounter almost no restrictions regarding the types of elements they can work with. It can simulate the properties of any combination of atoms, including molecules and crystal systems, as well as unknown materials.

Speed

高速 / High Speed イメージ
Over 10,000x faster than conventional methods

The atomistic simulation tasks that take hours to months using density functional theory (DFT) on a high-performance computer can be finished in only a few seconds using Matlantis.

User-Friendliness

使いやすさ / Easy to Use イメージ
Just open your browser to run simulations

Thanks to the pre-trained deep learning model, physical property calculation library, and high-performance computing environment, no hardware or software installations are required for performing simulations. Unlike conventional machine learning potentials, Matlantis requires no data collection or training by users.

Go to Product Detail