Machine Learning-Assisted Electrode Material Fabrication and Electrochemical Efficiency Prediction and Validation of PANI-Ni/Co Hydroxide Nanocomposites

Electrode materials play a critical role in the charge storage mechanism in supercapacitor devices. Hence, predicting the performance, namely, the specific capacitance (Csp), rate capability, and cyclic stability of these electrodes, is crucial for supercapacitor applications. In this report, a data-centric machine-learning (ML) approach was implemented to study the effect of polyaniline (PANI) on the electrochemical behavior of NiCo(OH)2. ML models were used to predict the combinational Csp-grade and Csp-value of composite electrode materials. The lowest relative percentage error of 1.04% for 3 PNC is observed between predicted and experimental Csp-values. The ML-predicted rate capability of 3 PNC strongly aligned with the experimental data when the current density is increased 4-fold and revealed better rate capability. The experimental Csp retention of 82.4% after 10000 cycles closely matched the predicted retention of 88.56%, with a percentage error of 6.92%. Hence, the ML may be implemented to predict the performance of other electrode materials for energy storage applications.