Performance analysis of Na-ion batteries by machine learning

Abstract

Herein, we analyze the effects of critical materials, electrode preparation methods, and operational descriptors on the discharge capacity and cycle life of the Na-ion batteries. An extensive dataset is constructed from literature and analyzed using machine learning. It is found that alloy-based anodes have the highest average discharge capacity, while the carbon group exhibits higher cycle life performance; it is also deduced that the discharge capacity is improved when alloy-based anodes are coupled with metal oxide cathodes. Random forest models are reasonably good for providing rough predictions for discharge capacity and demonstrating the relative significance of descriptors; the root mean square error for training and testing are 75 mAh/g and 157 mAh/g, respectively, for the anode, and 13 mAh/g and 48 mAh/g, respectively, for the cathode studies. Anode and cathode types are the most influential descriptors for the model, as expected, while the synthesis conditions and crystal structure are also effective. Decision tree classification of cycle life (cycle number at which 80% of peak discharge capacity is retained) is also quite successful in leading heuristic rules for electrode preparation. Material synthesis conditions are highly influential for high cycle life for the anode, while solvent selection seems to be also significant for cathode studies.