Integrating First Principles Calculations and Machine Learning to Study the ReNiO2/Ti3C2 Heterojunctions for Sodium Ion Batteries

Due to the large size of sodium ions and their slow redox kinetics in electrochemical processes, the sodium ion batteries currently are still far from satisfactory. This study investigates the electrical transport properties of ReNiO₂/ Ti₃C₂ heterojunctions in sodium ion batteries through a combination of first principles calculations and machine learning analysis. The ReNiO₂/Ti₃C₂ heterojunctions exhibit metallic characteristics and enhanced electronic conductivity due to the hybridization of p-d orbitals and the strengthening of Ni─Ti metal bonds. The sodium ion migration energy barrier decreases with increasing rare earth atomic number, facilitating ion transport. Machine learning analysis identifies key factors influencing ion and electron transport rates, including strain, lattice constants, and doping concentration. These findings provide theoretical guidance for designing more efficient negative electrodes for sodium ion batteries.