硅酸盐学报

基于固态电解质大原子模型的Li₆PS₅Cl中缺陷调控离子导率

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卢智浩 ¹^,² , 吴宏宇 ³ , 高于翔 ¹ , 钟志诚 ¹

硅酸盐学报 | 电化学储能材料计算与设计专题—研究论文 2025,53(7): 1920-1928

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硅酸盐学报 |电化学储能材料计算与设计专题—研究论文 2025 , 53 (7) : 1920 -1928

基于固态电解质大原子模型的Li₆PS₅Cl中缺陷调控离子导率

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卢智浩¹^,², 吴宏宇³, 高于翔¹, 钟志诚¹

作者信息

^1．中国科学技术大学人工智能与数据科学学院，江苏苏州 215123

^2．中国科学技术大学纳米科学技术学院，江苏苏州 215123

^3．苏州实验室，江苏苏州 215123

通讯作者:

钟志诚（1982—），男，博士，教授。

作者简介:

卢智浩（1998—），男，硕士研究生。

Defect Regulation of Ionic Conductivity in Li₆PS₅Cl Based on a Large-Atom Model for Solid-State Electrolytes

Zhihao LU¹^,², Hongyu WU³, Yuxiang GAO¹, Zhicheng ZHONG¹

Affiliations

^1.School of Artificial Intelligence and Data Science, University of Science and Technology of China, Suzhou 215123, Jiangsu, China

^2.School of Nano Science and Technology, University of Science and Technology of China, Suzhou 215123, Jiangsu China

^3.Suzhou Lab, Suzhou 215123, Jiangsu, China

Corresponding Author:

ZHONG Zhicheng (1982-), male, Ph.D., Professor. E-mail: zczhong@ustc.edu.cn

LU Zhihao (1998-), male, Master candidate. E-mail: luzhihao@mail.ustc.edu.cn

出版时间: 2025-07-20 doi: 10.14062/j.issn.0454-5648.20250089

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摘要

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Li₆PS₅Cl是一种具有高离子导率的固态电解质，其中复杂的缺陷是实现对其性能进一步调控的关键。然而，传统理论计算方法无法兼顾量子力学精度和大尺度计算，难以有效研究Li₆PS₅Cl复杂的缺陷态。本工作使用固态电解质大原子模型，通过少量缺陷态数据微调模型，并加以蒸馏构建力场模型。该模型可在模拟上千原子规模的各种缺陷态时兼顾量子力学精度。研究发现，低温下LiCl缺陷的引入使有序Li₆PS₅Cl呈现出一定离子导率，但高温下LiCl缺陷会导致体系不稳定，部分PS₄四面体开始分解，从而限制Li离子的迁移。

关键词

大原子模型 / 机器学习势 / 分子动力学 / 固态电解质 / 缺陷

Abstract

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Introduction

Solid-state electrolytes are critical for advancing lithium-ion battery technologies due to their potential to provide higher safety, improved energy density, and better long-term stability, compared to conventional liquid electrolytes. Among solid-state electrolytes, sulfide-based materials such as Li₆PS₅Cl (LPSC) are especially promising because of their high ionic conductivity, low interface impedance, and mechanical flexibility. LPSC has an argyrodite structure that inherently offers abundant lithium-ion migration pathways, ensuring efficient ionic transport. However, practical applications of LPSC are often hindered in the presence of intrinsic defects, such as lithium vacancies and LiCl substitutions, thus altering the local atomic environments and macroscopic ionic transport properties. Conventional computational methods like ab initio molecular dynamics (AIMD) offer a quantum-level precision, but are computationally prohibitive in large scales, whereas classical empirical potentials lack the accuracy to capture complex defect-induced local rearrangements. Therefore, developing a computational method that combines quantum accuracy with large-scale simulations remains an important challenge.

Methods

This study used a deep learning-based large-atom model (DPA-SSE), employing a "pretraining-fine-tuning-distillation" strategy specifically designed for sulfide solid electrolytes. Initially, the model was pretrained based on extensive density functional theory (DFT) data covering diverse sulfide materials and defect structures. Subsequently, it was fine-tuned using a carefully selected subset of 551 high-value DFT configurations specifically related to LPSC and its defect environments, chosen from a larger dataset of 11 631 configurations. These configurations included comprehensive DFT-calculated energies, atomic forces, and virial stress tensors. The fine-tuned model could achieve a quantum-level precision with a significantly reduced mean absolute error in predicting energies and forces. Finally, a distilled model was proposed via leveraging the fine-tuned model to label extensive datasets efficiently, enabling simulations of large-scale systems with thousands to millions of atoms in nanosecond time scales.

Results and discussion

The molecular dynamics simulations are conducted to systematically investigate the effect of LiCl defect concentration on the ionic conductivity in LPSC at different temperatures by the distilled DPA-SSE model. At a lower temperature (i.e., 333 K), the simulations show that introducing LiCl defects moderately improves the ionic conductivity, compared to pristine LPSC, indicating that defects at a low concentration can create a beneficial local disorder facilitating ion transport. However, at an elevated temperature (i.e., 500 K), the simulations clearly demonstrate a negative correlation between ionic conductivity and increasing defect concentrations. Specifically, the measured ionic conductivities at different defect concentrations (i.e., 0%, 3.8%, and 7.7%) are approximately 3.23 × 10^-2, 2.06 × 10^-2 S/cm, and 1.36 × 10^-2 S/cm, respectively. The reduced ionic conductivity at higher defect concentrations is attributed to an increased structural instability, leading to a partial decomposition or a significant rearrangement of PS4 tetrahedral units, which form critical frameworks for efficient ion migration. The radial distribution function (RDF) analysis further corroborates these findings, showing intensified local coordination and disrupted ion diffusion pathways at higher defect levels.

Conclusions

This work demonstrated the feasibility and effectiveness of using a deep learning-based large-atom model to accurately and efficiently simulate ionic transport in defect-rich sulfide solid electrolytes. The "pretraining-fine-tuning-distillation" approach significantly reduced the computational cost associated with conventional quantum mechanical simulations, while maintaining a high predictive accuracy. This study provided valuable theoretical insights that could inform the design and optimization of high-performance solid-state electrolytes via revealing the intricate relationship among defect concentrations, local structural dynamics and ionic conductivity. Moreover, the methodological framework presented could have a broad applicability, offering a powerful and scalable computational tool for studying a variety of complex materials in energy storage applications.

Key words

large-atom model / machine learning potential / molecular dynamics / solid-state electrolyte / defects

引用本文

卢智浩, 吴宏宇, 高于翔, 钟志诚. 基于固态电解质大原子模型的Li₆PS₅Cl中缺陷调控离子导率. 硅酸盐学报, 2025 , 53 (7) : 1920 -1928 . DOI: 10.14062/j.issn.0454-5648.20250089

Zhihao LU, Hongyu WU, Yuxiang GAO, Zhicheng ZHONG. Defect Regulation of Ionic Conductivity in Li₆PS₅Cl Based on a Large-Atom Model for Solid-State Electrolytes[J]. Journal of the Chinese Ceramic Society, 2025 , 53 (7) : 1920 -1928 . DOI: 10.14062/j.issn.0454-5648.20250089

基金

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国家重点研发计划(2022YFA1403000; 2021YFA0718900)
中国科学院前沿科学重点研究计划(ZDBS-LY-SLH008)
国家自然科学基金(12374096)

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2025年第53卷第7期

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文章信息

doi: 10.14062/j.issn.0454-5648.20250089

接收时间：2025-02-13
首发时间：2026-10-21
出版时间：2025-07-20

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出版历史

收稿日期：2025-02-13
修回日期：2025-03-14

基金

国家重点研发计划(2022YFA1403000; 2021YFA0718900)

中国科学院前沿科学重点研究计划(ZDBS-LY-SLH008)

国家自然科学基金(12374096)

作者信息

^1．中国科学技术大学人工智能与数据科学学院，江苏苏州 215123

^2．中国科学技术大学纳米科学技术学院，江苏苏州 215123

^3．苏州实验室，江苏苏州 215123

通讯作者:

钟志诚（1982—），男，博士，教授。

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https://castjournals.cast.org.cn/joweb/gsyxb/CN/10.14062/j.issn.0454-5648.20250089

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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