Machine Learning Assisted Prediction of Donor Numbers: Guiding Rational Fabrication of Polymer Electrolytes for Lithium-ion Batteries

Yuqing Gao; Shengguang Qi; Mianrui Li; Tongmei Ma; Huiyu Song; Zhiming Cui; Zhenxing Liang; Li Du

doi:10.1002/anie.202411437

Machine Learning Assisted Prediction of Donor Numbers: Guiding Rational Fabrication of Polymer Electrolytes for Lithium-ion Batteries

Angew Chem Int Ed Engl. 2024 Nov 28:e202411437. doi: 10.1002/anie.202411437. Online ahead of print.

Authors

Yuqing Gao¹, Shengguang Qi¹, Mianrui Li¹, Tongmei Ma², Huiyu Song¹, Zhiming Cui¹, Zhenxing Liang¹, Li Du³

Affiliations

¹ South China University of Technology, School of Chemistry and Chemical Engineering, CHINA.
² University of Science and Technology of China, School of Chemistry and Chemical Engineering, CHINA.
³ South China University of Technology, chemisrty, 381 Wushan Road Tianhe District, 510000, Guangzhou, CHINA.

PMID: 39606940
DOI: 10.1002/anie.202411437

Abstract

Polymer electrolytes are of interest in high-energy-density batteries. However, how the intrinsic electron-donating capability of polymer segments involved in coordination affects lithium-ion dissociation/transmission and rationally guides the design and fabrication of electrolytes is a highly exploratory topic. This study proposes a workable method that integrates machine learning with density functional theory to predict donor numbers (DN) for polymer building units. Using this approach, polymer chains with optimized DN are designed, effectively modulating the solvation structure of lithium-ion. Molecular dynamics simulations confirm the positive impact of polymer chains on rapid transport of lithium ions. Experimental validation of the proposed zwitterionic polymer electrolyte (ZPE) showcases satisfactory parameters: ion conductivity (0.59 mS cm-1), ion migration numbers (0.82), and activation energy (0.016 eV). Electrochemical analysis on Li|ZPE|Li symmetric batteries demonstrate sustained plating/stripping performance exceeding 3000 hours at a current density of 0.2 mA cm-2. Assembled NCM|ZPE|Li batteries exhibit stable cycling over 1400 cycles at 4.3 V, with a capacity retention ratio of 92.3%. Moreover, even under ultra-high voltages of 4.5 V and 4.7 V, NCM|ZPE|Li batteries display stable cycling performances. This approach offers a paradigmatic strategy for polymer molecule design, advancing sustainable battery technologies.

Keywords: Solvation structures; donor number; lithium-ion batteries; machine learning; polymer electrolytes.