Characterizing High-Capacity Janus Aminobenzene-Graphene Anode for Sodium-Ion Batteries with Machine Learning

TL;DR

This study uses machine learning and density-functional theory to analyze sodium storage mechanisms in Janus aminobenzene-graphene, revealing high capacity and fast ion transport properties for sodium-ion battery anodes.

Contribution

It introduces a novel MLFF-based simulation approach to characterize Na storage in Janus aminobenzene-graphene, demonstrating its potential as a high-capacity anode material.

Findings

Extended low-voltage plateau at 0.15 V vs. Na/Na+

Estimated gravimetric capacity of ~400 mAh g^-1

Na diffusivities of ~10^-6 cm^2 s^-1, much higher than hard carbon

Abstract

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.