机器学习模拟发现新型钠电负极可实现400mAh/g高容量且体积不变。
Characterizing High-Capacity Janus Aminobenzene-Graphene Anode for Sodium-Ion Batteries with Machine Learning
- 用机器学习力场结合第一性原理模拟钠存储机制
- 室温下容量达400mAh/g，电压平台低至0.15V，体积变化极小
- 适合追求高能量密度与长寿命的钠电材料研发者
钠离子电池需要兼具高容量、低工作电压、快速钠离子传输和机械稳定性的负极，传统负极难以满足。本文利用SpookyNet机器学习力场（MLFF）结合全电子密度泛函理论计算，研究了氨基苯功能化Janus石墨烯（Na$_x$AB）在室温下的钠存储行为。模拟显示其具有三阶段存储机制：氨基苯基团特异性吸附、Na$_n$@AB$_m$结构形成，以及层间通道填充，与硬碳的多孔、石墨层间及缺陷控制行为不同。该材料表现出0.15 V（vs. Na/Na⁺）的长低压平台，理论质量容量约400 mAh g⁻¹，体积变化可忽略，钠离子扩散系数约10⁻⁶ cm² s⁻¹，比硬碳高两到三个数量级。结果表明Janus氨基苯-石墨烯是一种有前景的结构明确的高容量钠电负极，展示了基于MLFF模拟在电极材料表征中的强大能力。
原文摘要 · Abstract (English)
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.
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