Sodium-ion batteries require anodes that combine high capacity, low operating voltage, fast Na-ion transport, and mechanical stability, which conventional anodes struggle to deliver. Here, we use the SpookyNet machine-learning force field (MLFF) together with all-electron density-functional theory calculations to characterize Na storage in aminobenzene-functionalized Janus graphene (Na$_x$AB) at room-temperature. Simulations across state of charge reveal a three-stage storage mechanism-site-specific adsorption at aminobenzene groups and Na$_n$@AB$_m$ structure formation, followed by interlayer gallery filling-contrasting the multi-stage pore-, graphite-interlayer-, and defect-controlled behavior in hard carbon. This leads to an OCV profile with an extended low-voltage plateau of 0.15 V vs. Na/Na$^{+}$, an estimated gravimetric capacity of $\sim$400 mAh g$^{-1}$, negligible volume change, and Na diffusivities of $\sim10^{-6}$ cm$^{2}$ s$^{-1}$, two to three orders of magnitude higher than in hard carbon. Our results establish Janus aminobenzene-graphene as a promising, structurally defined high-capacity Na-ion anode and illustrate the power of MLFF-based simulations for characterizing electrode materials.

翻译：钠离子电池需要兼具高容量、低工作电压、快速的钠离子传输和机械稳定性的负极材料，而传统负极难以同时满足这些要求。本文采用SpookyNet机器学习力场结合全电子密度泛函理论计算，在室温下表征了氨基苯功能化Janus石墨烯（Na$_x$AB）中的钠存储特性。跨荷电状态的模拟揭示了三阶段存储机制——氨基苯基团上的位点特异性吸附与Na$_n$@AB$_m$结构形成，随后是层间通道填充——这区别于硬碳中多阶段孔隙、石墨层间和缺陷控制的行为。该机制导致开路电压曲线呈现与Na/Na$^{+}$相比0.15 V的宽低压平台，估算重量容量约为400 mAh g$^{-1}$，体积变化可忽略不计，钠扩散系数约为10$^{-6}$ cm$^{2}$ s$^{-1}$，比硬碳高两到三个数量级。我们的研究结果确立了Janus氨基苯-石墨烯作为一种有前途的、结构明确的高容量钠离子负极材料，并展示了基于MLFF模拟在表征电极材料方面的强大能力。