Inhibition of lithium dendrite growth with highly concentrated ions: cellular automaton simulation and surrogate model with ensemble neural networks,Molecular Systems Design & Engineering

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Inhibition of lithium dendrite growth with highly concentrated ions: cellular automaton simulation and surrogate model with ensemble neural networks
Molecular Systems Design & Engineering ( IF 3.2 ) Pub Date : 2021-12-13 , DOI: 10.1039/d1me00150g
Tong Gao ₁ , Ziwei Qian ₁ , Hongbo Chen _{1,

2} , Reza Shahbazian-Yassar ₃ , Issei Nakamura ₁

Affiliation

We have developed a lattice Monte Carlo (MC) simulation based on the diffusion-limited aggregation model that accounts for the effect of the physical properties of small ions such as inorganic ions and large salt ions that mimic ionic liquids (ILs) on lithium dendrite growth. In our cellular automaton model, molecular and atomistic details are largely coarse-grained to reduce the number of model parameters. During lithium deposition, the cations of the salt and ILs form positively charged electrostatic shields around the tip of the dendrites, and the anions of the salt and ILs form negative local potential lumps in adjacent areas to the dendrite. Both of the effects change the distribution of the electrostatic potential and notably inhibit dendrite formation between electrodes. The applied voltage and the physical properties of the salt ions and ILs, such as the size of the ions, the size asymmetry between the cation and anion, the dielectric constant, the excluded volume of the ions, and the model parameter η, notably affect electric-field screening and hence the variation in the local potential, resulting in substantial changes in the aspect ratio and the average height of the dendrites. Our present results suggest that the large salts such as ILs more significantly inhibit the dendrite growth than the small ions, mainly because the ions highly dissociated in ILs can participate in electrostatic shielding to a greater degree. To reduce the computational complexity and burden of the MC simulation, we also constructed a surrogate model with ensemble neural networks.

中文翻译：

用高浓度离子抑制锂枝晶生长：元胞自动机模拟和具有集成神经网络的代理模型

我们开发了一种基于扩散限制聚集模型的晶格蒙特卡罗 (MC) 模拟，该模型考虑了模拟离子液体 (IL) 的无机离子和大盐离子等小离子的物理性质对锂枝晶生长的影响. 在我们的元胞自动机模型中，分子和原子细节在很大程度上是粗粒度的，以减少模型参数的数量。在锂沉积过程中，盐和离子液体的阳离子在枝晶尖端周围形成带正电荷的静电屏蔽，盐和离子液体的阴离子在枝晶附近区域形成负局部电位团。这两种效应都会改变静电势的分布，并显着抑制电极之间的枝晶形成。η，显着影响电场屏蔽，从而影响局部电位的变化，导致长宽比和枝晶平均高度的显着变化。我们目前的结果表明，像离子液体这样的大盐比小离子更能显着抑制枝晶生长，主要是因为离子液体中高度离解的离子可以更大程度地参与静电屏蔽。为了降低 MC 模拟的计算复杂度和负担，我们还构建了一个具有集成神经网络的代理模型。

更新日期：2021-12-21

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