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

2026-03-23

AI Summary

Sodium-ion batteries urgently require anode materials that simultaneously deliver high capacity, low operating voltage, fast ion transport, and structural stability. This study integrates the SpookyNet machine learning interatomic potential with all-electron density functional theory and room-temperature molecular dynamics simulations to uncover, for the first time, a three-stage sodium storage mechanism in aminobenzene-functionalized Janus graphene. The material exhibits a high specific capacity of approximately 400 mAh g⁻¹, a low voltage plateau at 0.15 V, negligible volume expansion, and an excellent Na⁺ diffusion coefficient of 10⁻⁶ cm² s⁻¹—significantly outperforming commercial hard carbon. This work not only elucidates a well-defined, high-capacity sodium storage pathway but also highlights the powerful potential of machine learning force fields in the rational design of advanced electrode materials.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Economics, Online Markets and Human Computation: Social networks and social learning

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.

Problem

• sodium-ion batteries
• anode materials
• high capacity
• low operating voltage
• mechanical stability

Innovation

• Janus graphene
• machine learning force field
• sodium-ion battery anode
• high-capacity storage mechanism
• SpookyNet