Machine Learning-Accelerated Development of Li-Rich Layered Oxides with High Reversible Capacity and Coulombic Efficiency.
basic_science · Level V
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- Record sourced from PubMed, PMID 41453153.
- Also identified by DOI 10.1021/acsnano.5c16117.
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Abstract
Li-rich layered oxides are promising high-energy-density cathodes for next-generation Li-ion batteries, yet their practical application is hindered by structural instability arising from oxygen redox activity, which typically results in a trade-off between achieving a high discharge specific capacity and maintaining a high Coulombic efficiency. Herein, we employ machine learning to identify key synthesis factors governing the initial Coulombic efficiency in Li<sub>1.2</sub>Ni<sub>0.13</sub>Co<sub>0.13</sub>Mn<sub>0.54</sub>O<sub>2</sub>. Four machine learning models were trained with a data set of 203 samples. Among them, the gradient boosting decision tree exhibited superior predictive accuracy (<i>R</i><sup>2</sup> = 0.802 on the test set) and identified the lithium-to-transition metal ratio and presintering atmosphere as critical parameters. Machine learning-guided synthesis reveals that presintering in air at low temperatures reduces the Li<sub>2</sub>MnO<sub>3</sub> phase proportion, promotes the exposure of the {010} planes favorable for Li<sup>+</sup> transport, and mitigates the formation of surface rock-salt. This approach yielded a high discharge capacity of 301.02 mAh g<sup>-1</sup> and an initial Coulombic efficiency of 81.05%, highlighting the effective integration of data-driven design with experimental synthesis for advanced Li-rich layered oxide optimization.