Study on electrochemical properties of NbnC/SiC(n = 1,2) heterojunction by first principles combined with machine learning
摘要
This study presents a systematic investigation combining density functional theory calculations and machine learning approaches to elucidate sodium adsorption behavior on Nb2C, NbC, SiC monolayers and their heterostructures (Nb2C/SiC and NbC/SiC). The research establishes fundamental correlations between charge transfer, adsorption distance, and concentration-dependent stability, while developing a clustered linear regression (CLR) model incorporating material/adsorption-site-dependent electronic dipole moment descriptors for physically interpretable adsorption energy prediction. The electrochemical performance evaluation reveals exceptional properties for both heterostructures: ultralow diffusion barriers (0.234 eV for Nb2C/SiC and 0.156 eV for NbC/SiC), high theoretical capacities (450.63 and 369.69 mAh/g respectively), and safe operating voltage windows (0.62–0.73 V). These characteristics demonstrate an optimal balance between fast ionic transport and high sodium storage capability, positioning these heterostructures as promising anode candidates for sodium-ion batteries.