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Insight Gained from Using Machine Learning Techniques to Predict the Discharge Capacities of Doped Spinel Cathode Materials for Lithium‐Ion Batteries Applications
Author(s) -
Wang Guanyu,
Fearn Tom,
Wang Tengyao,
Choy Kwang-Leong
Publication year - 2021
Publication title -
energy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.91
H-Index - 44
eISSN - 2194-4296
pISSN - 2194-4288
DOI - 10.1002/ente.202100053
Subject(s) - spinel , electronegativity , materials science , doping , dopant , electrochemistry , molar mass , cathode , analytical chemistry (journal) , optoelectronics , composite material , chemistry , electrode , metallurgy , polymer , chromatography , organic chemistry
The electrochemical potentials of spinel lithium manganese oxide (LMO) have long been plagued by the significant Mn 3+ 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 20 th 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.