Machine learning prediction of the reversible capacities of a biomass-derived hard carbon anode for sodium-ion batteries

Abstract

This project is among the pioneering works that incorporate machine learning (ML) modeling into the development of biomass-derived sodium-ion battery anodes for sustainable energy storage technologies. It was conceptualized and executed to satisfy a desire to use computational techniques to fill the research gap in a paper authored by Meenatchi et al. in 2021. The authors asserted that an activated orange peel-derived hard carbon (AOPDHC) can be used as an anode for sodium-ion batteries, yet the evidence for this claim was lacking. This work therefore sought to utilize ML to verify the claim by investigating the reversible capacities of AOPDHC at different initial coulombic efficiencies (ICE) and current densities. Data used to train the algorithms were mined from literature and applied in a 4 : 1 training-to-testing data split. Models that gave good correlations between experimental and predicted capacities for some assumed unknowns were used to predict the reversible capacities of AOPDHC. The maximum capacity obtained for AOPDHC was 341.1 mA h g⁻¹ at a current density of 100 mA g⁻¹ and an ICE of 48% and the minimum capacity was 170.3 mA h g⁻¹ at a current density of 100 mA g⁻¹ and an ICE of 43%. Lastly, the modeling found ICE to be a very important factor that influences the reversible capacities of hard carbon anodes for sodium-ion batteries, which matches literature findings, and possibly validates the modeling procedure. This study is of utmost importance since biomass-derived hard carbons are versatile, cost-effective, environmentally friendly and sustainable.