全固态电池的固态电解质:机器学习驱动的发现
Jiwon Sun1,2, JunHo Song1, Juo Kim1,3
1School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.
ACS applied materials & interfaces
|August 25, 2025
概括
这项研究引入了一种机器学习框架,用于快速选高固态电解质的全固态电池. 该方法确定了具有较高离子导电性的有前途的石榴类材料,用于更安全的高能电池.
科学领域:
- 材料科学
- 电化学
- 计算化学
背景情况:
- 高固态电解质 (HE SSEs) 为全固态电池 (ASSB) 提供了更好的性能和安全性.
- 高级社会实验室的巨大化学空间和复杂性挑战了传统的实验和计算选方法.
- 加速发现新的HE SSE对于推进电池技术至关重要.
研究的目的:
- 开发和应用一种基于机器学习 (ML) 的新方法来有效选高异常 (CDHE) 石榴类型的SSE候选物.
- 加快探索有前途的HE SSE,降低计算成本并提高效率.
- 识别具有高离子导电性的新型CDHE石榴石类材料.
主要方法:
- 使用基于ML的替代模型,根据电子导电性和热力学稳定性选4348个CDHE石榴石类型的SSE候选物.
- 采用晶体哈密尔顿图神经网络 (CHGNet) 来确定稳定的原子配置,并计算树突抑制的弹性性质.
- 通过微调CHGNet潜力进行分子动力学 (MD) 模拟,以评估扩散和离子导电性.
主要成果:
- 成功选了CDHE石榴石类型的SSE候选数据集,对电子导电性和热力学有利性进行过.
- 确定了具有良好的弹性性质的候选材料,表明其可能抑制树突形成并确保界面稳定性.
- 通过MD模拟证实了三个有前途的CDHE石榴石类型的SSE候选物,其离子导电率在室温下大于10−4 S/cm.
结论:
- 开发的基于ML的选框架显著加快了高性能HE SSE的发现.
- 已识别的CDHE石榴石类材料显示出下一代全固态电池的潜力.
- 这种方法提供了一个计算效率高的途径来探索用于储能应用的复杂材料系统.
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