使用机器学习模拟器在氧化混合离子玻璃玻璃中进行扩散
Shingo Urata1, Noriyoshi Kayaba2
1Innovative Technology Laboratories, AGC Inc., Yokohama, Kanagawa 230-0045, Japan.
The Journal of chemical physics
|October 1, 2024
概括
将酸玻璃添加,可以提高离子的移动性,这对于固态电池接口材料至关重要. 机器学习模拟显示,当含量更高时,离子扩散增加.
科学领域:
- 材料科学 材料科学 材料科学
- 固态化学 固态化学
- 计算材料科学科学 计算材料科学
背景情况:
- 酸玻璃是固态电池的有希望的接口材料,因为它们的可塑性.
- 众所周知,的结合提高了酸盐玻璃中的电子导电性.
研究的目的:
- 为了研究兴奋剂对动态在酸盐和酸盐玻璃的影响.
- 为了验证机器学习原子间电位 (MLIP) 的准确性,用于建模这些玻璃系统.
主要方法:
- 分子动力学 (MD) 模拟利用机器学习的原子间潜力 (MLIP).
- 与实验数据对比MLIP准确性的验证,包括密度,中子衍射S(q) 和玻璃过渡温度 (Tg).
- 对结构性质和离子扩散机制的分析.
主要成果:
- 在MLIP-MD模拟中显示,酸盐 (LBCl) 和酸盐 (LBSCl) 玻璃中的离子扩散增加,含量更高.
- 结构分析显示,主要与离子相互作用,增强它们的移动性.
- 虽然密度预测需要NVT-then-NPT组合放松,但模型显示了合理的准确性.
结论:
- MLIP适用于模拟含的酸盐玻璃,并研究对离子导电性的影响.
- 增加的含量适度提高离子的移动性,有利于电池应用.
- 需要对MLIP进行进一步的改进,以准确地捕捉这些玻璃的中距离结构 (S(q)).
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