Data-driven design of electrolyte additives supporting high-performance 5 V LiNi<sub>0.5</sub>Mn<sub>1.5</sub>O<sub>4</sub> positive electrodes.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 40210879.
- Also identified by DOI 10.1038/s41467-025-57961-w and PMC identifier 11986164.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
LiNi<sub>0.5</sub>Mn<sub>1.5</sub>O<sub>4</sub> (LNMO) is a high-capacity spinel-structured material with an average lithiation/de-lithiation potential at ca. 4.6-4.7 V vs Li<sup>+</sup>/Li, far exceeding the stability limits of electrolytes. An efficient way to enable LNMO in lithium-ion batteries is to reformulate an electrolyte composition that stabilizes both graphitic (Gr) negative electrode with solid-electrolyte-interphase and LNMO with cathode-electrolyte-interphase. In this study, we select and test a diverse collection of 28 single and dual additives for the Gr||LNMO battery system. Subsequently, we train machine learning models on this dataset and employ the trained models to suggest 6 binary compositions out of 125, based on predicted final area-specific-impedance, impedance rise, and final specific-capacity. Such machine learning-generated new additives outperform the initial dataset. This finding not only underscores the efficacy of machine learning in identifying materials in a highly complicated application space but also showcases an accelerated material discovery workflow that directly integrates data-driven methods with battery testing experiments.