从构成到离子导电:以机器学习为指导,发现和实验验证Argyrodite型离子电解质
Songjia Kong1, Ziheng Yu1, Naoki Matsui2
1Department of Chemical Science and Engineering, School of Materials and Chemical Technology, Institute of Science Tokyo, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan.
Small (Weinheim an der Bergstrasse, Germany)
|November 19, 2025
概括
本研究介绍了一种机器学习框架,以根据元素组成预测固态电解质中的离子导电性. 这加速了对高性能固态电池的先进材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 计算化学的计算化学
背景情况:
- 推进全固态电池需要具有高离子导电性的固态电解质 (SSEs).
- 以前的研究主要集中在SSEs的结构驱动设计上.
- 基于构成的方法为材料发现提供了新的途径.
研究的目的:
- 开发一种机器学习框架 (Elements-To-Ionics,E2I) 用于预测类型SSE中的离子导电性.
- 仅使用基本信息来优化 SSE 构成.
- 加速发现用于固态电池的高性能SSE.
主要方法:
- 开发了一个基于构成的机器学习框架,Elements-To-Ionics (E2I).
- 使用E2I来预测和指导Si-Sn,Ge-Si和Ge-Sn共同替代的argyrodites的合成.使用E2I预测和指导Si-Sn,Ge-Si和Ge-Sn共同替代的argyrodites的合成.
- 采用热压技术来优化离子导电性.
主要成果:
- 合成的 argyrodites 与Li6.7Ge0.595Si0.105P0.3S5I表现出高的离子导电性 (7.2 × 10-3 S cm-1) 和低的激活能量 (0.20 eV).
- 在优化后,获得了与Li10GeP2S12型超离子导体 (>10-2 S cm-1) 相比的离子导电性.
- E2I模型准确地识别了高和低导电性的区域,减少了实验的努力.
结论:
- 基于构成的机器学习加速了高性能SSE的发现.
- E2I框架为开发下一代固态电池技术提供了有价值的方法.
- 这种信息学方法有效地在复杂的化学空间中导航新型电解质材料.
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