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预测固态电池接口的反应性和被动性

Eder G Lomeli^1,2, Brandi Ransom¹, Akash Ramdas¹

¹Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States.

ACS applied materials & interfaces

|September 15, 2024

概括

一个新的数据驱动模型预测了固态电解质和金属阳极接口的反应性,识别了300多种稳定的材料. 这种方法通过结合动力学和热力学来加速材料的发现,扩大了有希望的固态电解质池.

科学领域:

材料科学材料科学材料科学
计算化学计算化学
电化学电化学电化学

背景情况:

预测固态电解质和金属阳极接口的反应性对于固态电池的开发至关重要.
像密度函数理论 (DFT) 能量学和热力学凸船体计算等传统方法在捕捉动力学和动态界面行为方面存在局限性.
初始分子动力学 (AIMD) 模拟提供了详细的见解,但在计算上昂贵,大规模选需要大量时间.

研究的目的:

开发一种计算成本低廉的数据驱动模型,用于预测固态电解质和金属阳极之间的接口的反应性.
利用AIMD模拟数据的机器学习来捕捉控制界面稳定性的动力和热力学因素.
加速发现和选用于离子电池的新型,稳定的固态电解质材料.

主要方法:

在固体电解质-金属接口的原子结构信息和AIMD模拟数据上训练机器学习模型.
利用训练的模型,快速预测数千种候选材料的界面反应性.
将模型预测与传统热力学方法进行比较,以确定差异和新型稳定材料.

主要成果:

鉴定了300多种新的化学稳定的固体电解质和780多种被动化的固体电解质,预计它们在热力学上是不利的.
证明纯热力学方法可能会将许多潜在的固态电解质候选物错误标记为不稳定.
突出两个酸盐材料 (LiB13C2和LiB12PC),该模型预测并由AIMD证实,它们具有高导电性和与的化学稳定性.
这表明,有前途的,Li-稳定的固态电解质材料的池比以前估计的要大得多.

结论:

开发的数据驱动模型提供了一个计算效率高,准确的方法来预测固态电解质-金属界面反应性.
这种方法显著加速了材料的发现和选,克服了传统计算方法的局限性.
这些发现扩大了潜在的固态电解质材料的范围,为下一代固态电池铺平了道路.

关键词:

在AIMD中,我们可以使用AIMD.在 DFT 方面,它是最重要的.机器学习是机器学习.材料的发现发现材料的发现.固体电解质是一种固体电解质.固态电池是一种固态电池.

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