Insight Gained from Using Machine Learning Techniques to Predict the Discharge Capacities of Doped Spinel Cathode Materials for Lithium‐Ion Batteries Applications

Abstract

The electrochemical potentials of spinel lithium manganese oxide (LMO) have long been plagued by the significant Mn3+ dissolution during long cycle discharging, resulting in rapid capacity fading and short cycle life. Although the doping mechanisms are effective in suppressing these reactions, the correlations of their effects on the material properties and the improved discharging performance still remain uncovered. In this study, seven machine learning (ML) methods are applied to a manually curated dataset of 102 doped LMO spinel systems to predict the initial discharge capacities (IC) and 20th cycle end discharge capacities (EC) from fundamental system properties like material molar mass and crystal structure dimension. Gradient boosting models achieved the best prediction powers for IC and EC with their errors estimated to be 11.90 and 11.77 mAhg−1, respectively. Besides, a higher formula molar mass of doped LMO can improve both capacities and additionally, a shorter crystal lattice dimension with a dopant with smaller electronegativity can slightly improve the value of the IC and EC, respectively. This study demonstrates the great potential of using ML models to both predict the discharging performance of doped spinel cathodes and identify the governing material properties for controlling the discharging performance.

A data-driven interpretable method to predict capacities of metal ion doped TiO2 anode materials for lithium-ion batteries using machine learning classifiers

“…The result is the same as the conclusion of some studies on the doped cathode materials. 32,38 However, the internal correlations can still be found in some previously reported information. For example, Darr et al reported that a lower potential likely leads to the electrochemical activation of Sn-doped TiO 2 , which indicates higher capacity for rate performance.…”

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“…They found that GBM is the best model and showed that doped NMC materials with higher lithium amount, dopant atoms with lower electronegativities, and a smaller dopant amount would give better capacity properties. In addition, the discharge capacity of spinel lithium magnesium oxide (LMO) was also predicted by Wang et al 47 using ridge regression (RR), lasso regression (LaR), SVM, DNN, DT, RF and GBM. They found that doped LMO with higher formula molar mass, as well as a shorter crystal lattice dimension with dopants having smaller electronegativities can improve the capacities of LMO.…”

The Impact of Calendering Process Variables on the Impedance and Capacity Fade of Lithium‐Ion Cells: An Explainable Machine Learning Approach

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