Suppr超能文献

通过施加力对电解质迁移率矩阵进行高效模拟。

Efficient simulations of mobility matrices for electrolytes by applying forces.

作者信息

Tripathi Pramudit, Milner Scott T

机构信息

Department of Chemical Engineering, The Pennsylvania State University University Park Pennsylvania 16802 USA

Department of Materials Science and Engineering, The Pennsylvania State University University Park Pennsylvania 16802 USA.

出版信息

Chem Sci. 2024 Sep 13;15(39):16176-85. doi: 10.1039/d4sc03325f.

Abstract

Ion drift velocities in response to electric fields are a critical attribute of battery electrolytes. Accurately predicting species mobilities in such systems is an important challenge for atomistic simulations. In this work, we investigate two organic liquid electrolytes: LiPF dissolved in (a) dimethyl carbonate (DMC) and (b) a mixture of DMC and ethylene carbonate (EC). We compare two approaches to measure mobilities: observing center of mass diffusion with no forces applied, and observing species drift in response to external forces. The two approaches are related by the fluctuation-dissipation theorem, but they are not equally efficient computationally. We argue that statistical errors of the two methods scale differently with system size and simulation run time. In a head-to-head test, we apply both methods to LiPF in DMC in multiple simulations with the same size and run time. The drift method gives a much smaller variance in repeated measurements than the diffusion method, and should be preferred in practice.

摘要

响应电场的离子漂移速度是电池电解质的一个关键属性。准确预测此类系统中的物种迁移率是原子模拟面临的一项重要挑战。在这项工作中,我们研究了两种有机液体电解质:溶解在(a)碳酸二甲酯(DMC)和(b)DMC与碳酸亚乙酯(EC)混合物中的LiPF。我们比较了两种测量迁移率的方法:在不施加力的情况下观察质心扩散,以及观察物种对外部力的响应漂移。这两种方法通过涨落耗散定理相关联,但它们在计算效率上并不相同。我们认为这两种方法的统计误差随系统大小和模拟运行时间的缩放方式不同。在一项直接比较测试中,我们在多个具有相同大小和运行时间的模拟中将这两种方法应用于DMC中的LiPF。漂移方法在重复测量中给出的方差比扩散方法小得多,在实际应用中应优先选择。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f29b/11463325/1ac466ea7eef/d4sc03325f-f1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验