Electric-field-reinforced affinitive electrolytes for highly reversible aqueous zinc metal batteries

We propose an affinitive additive strategy featuring high donor number and dipole moment, exemplified by N,N-dimethylurea (DMU), to dynamically modulate Zn2+ solvation and the structure of the electric double layer under operational electric fields.  And this repository contains all computational data underlying the study, including: DFT-optimized molecular structures; initial and equilibrated configurations from MD simulations of bulk electrolyte and Zn/electrolyte interfaces; RDF and MSD analysis files; high-throughput xTB results (AIE, AEA, ion-binding energies); and machine learning scripts for descriptor screening. Data are organized by module with detailed READMEs to ensure full reproducibility.

我们提出了一种兼具高供体数与偶极矩的亲和型添加剂策略，以N,N-二甲基脲（N,N-dimethylurea, DMU）为代表，用于动态调控实际工作电场下锌离子（Zn²+）的溶剂化过程与双电层结构。本数据集仓库包含本研究依托的全部计算数据，具体包括：经密度泛函理论（Density Functional Theory, DFT）优化的分子结构；本体电解质与锌/电解质界面的分子动力学（Molecular Dynamics, MD）模拟初始构型及平衡构型；径向分布函数（Radial Distribution Function, RDF）与均方位移（Mean Squared Displacement, MSD）分析文件；高通量xTB计算结果（含绝热电离能（Adiabatic Ionization Energy, AIE）、绝热电子亲和能（Adiabatic Electron Affinity, AEA）及离子结合能）；以及用于特征描述符筛选的机器学习脚本。所有数据按模块进行组织，并附带详细的README文档，以确保研究结果可完全复现。