使用机器学习的原子间潜能,研究无形LiPON中的离子扩散性
Aqshat Seth1, Rutvij Pankaj Kulkarni1, Gopalakrishnan Sai Gautam1
1Department of Materials Engineering, Indian Institute of Science, Bengaluru 560012, India.
ACS materials Au
|May 19, 2025
概括
机器学习的潜能准确地模拟了氧化 (LiPON) 的无形结构和离子运输. 这种方法克服了计算挑战,揭示了薄膜电池应用的微小接口阻抗.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 固态电化学 固态电化学
背景情况:
- 氧化 (LiPON) 对于薄膜固态电池至关重要,因为它具有无形固体电解质特性.
- 在无形LiPON中以及跨LiLiLiLiPON接口的Li+运输模型是具有计算挑战性的,因为材料复杂性和尺度要求.
研究的目的:
- 开发和验证一种机器学习的原子间潜力 (MLIP),用于精确模拟LiPON.
- 通过使用开发的MLIP来研究散装LiPON中的Li+运输和LiLiLiPON接口中的Li.
主要方法:
- 通过使用13,454个密度函数理论 (DFT) 结构的大数据集,训练了一个神经等价原子间潜力 (NequIP) 框架.
- 通过低能量和力误差验证了MLIP的准确性,与DFT相比.
- 利用训练的潜力进行分子动力学模拟的批量LiPON和LiHideLiPON接口.
主要成果:
- 生成的无形LiPON结构与ab initio分子动力学一致,显示的结合.
- 在散装LiPON中模拟Li+扩散性,与现有文献有很好的一致性.
- 观察到Li+运输在Li(110) 下载的LiPON和Li(111) 下载的LiPON接口是比散装相缓慢一个数量级,但具有较小的异构性和没有显著的阻抗积累.
结论:
- 机器学习潜能,特别是NequIP,对于像LiPON这样复杂无形材料的高保真大规模建模是非常有效的.
- 开发的MLIP允许在基于LiPON的系统中有效调查Li+运输机制.
- 结果表明界面阻抗最小,支持LiPON在先进的薄膜设备中的使用.
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