Ensemble Machine Learning for Predictive Accuracy and Experimental Corroboration on transition metal-based electrodes for Supercapacitor

Abstract

Machine learning (ML) proves highly effective in refining the electrochemical performance of electrode materials through precise tuning of compositional parameters. This study employs a data-driven ensemble learning approach, integrating classification and regression techniques to investigate the electrochemical behavior of transition metal-based electrodes with varying Ni:Co stoichiometry. A resample filter is introduced to the dataset to enhance predictive accuracy, yielding closely aligned classification and regression predictions. ML predictions discern that Ni-rich electrodes are apt for high-capacity, while Co-rich excels in high-rate applications. The characterizations of developed electrode materials XRD, FT-IR, EDS, and XPS confirmed the formation of desired compositions. The XPS analysis indicates that nickel and cobalt are in variable Ni2+/Ni3+ and Co2+/Co3+ oxidation states, enabling a reversible charge storage mechanism, while phosphorus is present in the pentavalent state. The TEM micrograph suggests that the nanoparticle size is extremely small, in the range of 1-3 nm, which is significant for providing a high surface-to-volume ratio, thereby enhancing specific capacitance (Csp). Experimental findings highlight N1C2 as the superior electrode, exhibiting impressive capacitance (2247.6 F g−1 at 3 A g−1), robust rate retention (78.6% from 3 to 20 A g−1), and substantial capacitance retention (91.3% after 10,000 charge-discharge cycles), which is in line with ML prediction. The experimental validation highlights the excellent agreement (percentage error: 2.48 to 8.46%) between the capacitance that was predicted, and the capacitance achieved experimentally. Predicted rate and cyclic retention curves align closely with experimental results, with minimal errors (0.03 to 19.3% and 3.95 to 19.64%, respectively). Lastly, the introduction of reverse structural and operational feature engineering contributes to the optimization of electrode material performance.