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.
Highlights
Rechargeable lithium-ion batteries (LIBs) are known as the most promising energy storage technology due to their high energy density, high power density, and long charge/discharge life cycle.[1,2] Presently, an extensive amount of research has been methods are applied to a manually curated dataset of 102 doped lithium manganese oxide (LMO) spinel devoted to boosting their performance as to 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
One’s intuition should not be misguided by the perfect negative linearity (R 1⁄4 –1) relationship estimated for the M and manganese atom (Mn) as it is the result of direct site substitution
We have explored the scope of seven algorithms for their prediction power in describing the correlations of six different structural and elemental properties of 102 doped LMO systems and the corresponding discharging performance at the first and 20th cycles
Summary
The distributions of collected IC and EC values of 102 doped spinel cathodes are shown in Figure S1, Supporting Information. The ratio of binder and additive can affect the mechanical stability and the conductivities of the cathode material; this would have an impact in predicting the long-term discharge capacities.[32] due to the complexity in curating such information while considering that the active LMO component being the main driving force for the electrochemical reactions, such mixing ratio is assumed to be standardized during the data collection and is not involved in the model construction. Www.entechnol.de approach: 1) Standardize the cell fabrication methods for anode, cathode, electrolyte, and report the experimental parameters in detail, including the mixing ratio of the raw materials. 2) Report the full results of the electrode features such as the X-ray diffraction (XRD) results, physical dimensions, crystal structure distribution, particle size and their distribution, and the pore sizes in “csv” format so that it would be more assessable to ML models. 3) Use the principal component analysis method to reduce the dimensions of the descriptor space prior to the model training to improve the training speed and prediction accuracy. 4) Discover new descriptors for describing the material properties, for example, using density functional theory to estimate the dipole moment
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