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+title: "What Can DP Do too? | DeePMD Facilitates the Study of Ion Diffusion Mechanism in Li₆PS₅Cl Solid Electrolyte"
+date: 2025-01-16
+categories:
+- DeePMD-kit
+---
+
+Recently, a research team led by Professor Yi He and Associate Researcher Nan Xu from Zhejiang University/Zhejiang University Quzhou Institute used the deep potential molecular dynamics (DeePMD) method to conduct an in-depth study of the lithium-ion diffusion mechanism in the solid electrolyte Li₆PS₅Cl. The relevant research results were published in the well-known materials journal Chemistry of Materials under the title "Insights into the Atomic Mechanism of Lithium-Ion Diffusion in Li₆PS₅Cl via a Machine Learning Potential" (DOI: 10.1021/acs.chemmater.4c01152), with master student Jiafeng Chen as the first author.
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+
+## Research Background
+
+Solid electrolytes (SSEs) are the core and key components of all-solid-state lithium batteries, and their ionic conductivity largely determines the performance of the battery. Among many solid electrolyte materials, Li₆PS₅Cl has attracted much attention due to its good application prospects. However, the ionic conductivity of this material is significantly affected by the disordered distribution of S and Cl at the 4d and 4a sites in the crystal structure. In existing studies, researchers have used the hopping rate analysis and mean square displacement (MSD) analysis methods to study the influence of the S/Cl anion disorder on the ionic conductivity of Li₆PS₅Cl. However, there are obvious inconsistencies in the preferred ranges of S/Cl anion disorder obtained by these two methods, which seriously hinder the in-depth understanding and regulation of the ion conduction mechanism of this material. To solve this problem, this study used the effective hopping rate analysis method in molecular dynamics simulations based on a self-trained machine learning potential and successfully obtained a more in-depth and comprehensive understanding of the lithium-ion diffusion mechanism in Li₆PS₅Cl. The results show that the key to improving the ionic conductivity is to increase the effective hopping rate in the rate-limiting pathway. This achievement not only provides a theoretical basis for further optimizing the ionic conductivity of this material but also provides valuable references for studying the factors that regulate the ionic conductivity in other solid electrolytes.
+
+## Research Results
+
+In the crystal structure of Li₆PS₅Cl (Figure 1), S anions completely occupy the 4d sites, and Cl anions completely occupy the 4d sites, forming the ordered structure of this material. Lithium ions are distributed in the lithium cages formed by the 48h sites centered on the 4d sites and form three diffusion pathways: doublet, intracage, and intercage. Since S and Cl anions have similar radii, they are prone to disordered distribution at the 4d and 4a sites. The disorder of S/Cl anions, that is, the proportion of Cl at the 4d sites, has an important impact on the diffusion behavior of lithium ions.
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/DeePMD_16_01_2025/pic1.png pic_center width="60%" height="60%" /></center>
+
+*Figure 1 (a) Crystal structure of Li₆PS₅Cl. For clarity, the S ions at the 16e position are omitted. (b) Structure of the lithium ion cage.*
+
+The research team first constructed a Deep Potential (DP) applicable to a wide range of S/Cl disorder in Li₆PS₅Cl. After the initial dataset construction and active learning stages, a sufficient dataset was obtained and divided into a training set and a validation set at a ratio of 9:1. The trained DP has an accuracy comparable to DFT in predicting energy, force, and virial properties (Figure 2).
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/DeePMD_16_01_2025/pic2.png pic_center width="60%" height="60%" /></center>
+
+*Figure 2 (a) Changes in the loss function during training. (b-d) Diagonal plots of energy, force, and virial in the validation set.*
+
+Subsequently, DPMD simulations of Li₆PS₅Cl with different S/Cl disorder degrees showed that the ionic conductivity of the Li₆PS₅Cl solid electrolyte increases with the increase of S/Cl disorder degree until it reaches the preferred range of 37.5%-50%, and then decreases with the further increase of disorder degree, as shown in Figure 3.
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/DeePMD_16_01_2025/pic3.png pic_center width="60%" height="60%" /></center>
+
+*Figure 3 Room-temperature ionic conductivity and activation energy of Li₆PS₅Cl with different S/Cl anion disorder levels*
+
+The analysis of the total hopping rates of lithium ions in the doublet, intracage, and intercage pathways (Figure 4a) shows that there is an obvious difference between the optimal disorder degree obtained from the rate-limiting pathway with the lowest hopping rate and that in Figure 3. Therefore, the concept of effective hopping rate was introduced in this study to exclude the back-and-forth hopping that does not contribute to the overall diffusion, thus accurately describing the ion diffusion law (Figure 4b). By analyzing the changes in the rate-limiting pathway of the effective hopping rate, the same optimal disorder degree range of 37.5%-50% as in Figure 3 was obtained, as shown in Figure 4b.
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/DeePMD_16_01_2025/pic4.png pic_center width="70%" height="70%" /></center>
+
+*Figure 4 (a) Total hopping rates of different hopping types. (b) Effective hopping rates of different hopping types. (c) Effective factors (effective hopping rate/total hopping rate) of different hopping types.*
+
+Based on the changes in the effective hopping rates of different pathways, this study explained the influence of S/Cl anion disorder on the continuity of the diffusion pathway (Figure 5). At 50% disorder degree, a continuous diffusion network produces high ionic conductivity, while too low and too high disorder degrees cause the diffusion network to break and result in low ionic conductivity.
+
+<center><img src=https://dp-public.oss-cn-beijing.aliyuncs.com/community/Blog%20Files/DeePMD_16_01_2025/pic5.png pic_center width="70%" height="70%" /></center>
+
+*Figure 5 Schematic diagrams of the connectivity of different diffusion pathways at (a) 0%, (b) 50%, and (c) 100% S/Cl anion disorder degrees. Solid lines, dashed lines, and transparent dashed lines represent high, medium, and low effective hopping rates, respectively.*
+
+## Conclusions
+
+This study investigated the influence of S/Cl anion disorder on lithium-ion diffusion by developing a machine learning potential. The results show that the ionic conductivity gradually increases in the S/Cl disorder degree range of 0%-50% and gradually decreases in the range of 50%-100%. The optimal S/Cl disorder degree range is 37.5%-50%, corresponding to a high effective hopping rate in the rate-limiting pathway. Too low and too high S/Cl disorder degrees will lead to low effective hopping rates in the intercage and doublet pathways, respectively, causing the diffusion network to break and resulting in low ionic conductivity. This study not only clarifies the atomic mechanism of lithium-ion diffusion in Li₆PS₅Cl but also reveals the importance of the effective hopping rate in the rate-limiting pathway for achieving high ionic conductivity. By linking ionic conductivity with the microscopic diffusion mechanism, this work provides a theoretical reference for designing solid electrolytes with high ionic conductivity.
+
+*Original link: https://pubs.acs.org/doi/10.1021/acs.chemmater.4c01152*
+
+## References
+
+[1] De Klerk, N. J. J.; Rosłon, I.; Wagemaker, M. Diffusion Mechanism of Li Argyrodite Solid Electrolytes for Li-Ion Batteries and Prediction of Optimized Halogen Doping: The Effect of Li Vacancies, Halogens, and Halogen Disorder. Chem. Mater. 2016, 28, 7955−7963.
+
+[2] De Klerk, N. J. J.; Van Der Maas, E.; Wagemaker, M. Analysis of Diffusion in Solid-State Electrolytes through MD Simulations, Improvement of the Li-Ion Conductivity in β-Li3PS4 as an Example. ACS Appl. Energy Mater. 2018, 1, 3230−3242.
+
+[3] Lee, J.; Ju, S.; Hwang, S.; You, J.; Jung, J.; Kang, Y.; Han, S. Disorder-Dependent Li Diffusion in Li6 PS5 Cl Investigated by Machine-Learning Potential. ACS Appl. Mater. Interfaces 2024, 16, 46442−46453.