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Updated: Sep 19, 2025

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Multitarget Generate Electrolyte Additive for Lithium Metal Batteries.

Xiangyang Liu1, Jianchun Chu1, Sa Xue1

  • 1Key Laboratory of Thermal Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.

Advanced Materials (Deerfield Beach, Fla.)
|June 19, 2025
PubMed
Summary

A new deep learning model optimizes electrolyte additives for safer, high-performance lithium metal batteries (LMBs). It discovered DFEPN, a novel additive enhancing capacity retention and flame resistance.

Keywords:
artificial intelligenceelectrolytegenerative modellithium batterymolecular design

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Artificial Intelligence

Background:

  • Electrolyte additives are vital for commercializing lithium metal batteries (LMBs).
  • Designing effective additives is challenging due to conflicting requirements like electrochemical performance and nonflammability.
  • Data scarcity hinders the development of novel electrolyte additives.

Purpose of the Study:

  • To develop a deep learning-assisted generative model for multiobjective optimization of electrolyte additives.
  • To overcome data scarcity issues in designing advanced battery materials.
  • To discover novel electrolyte additives with improved safety and electrochemical performance.

Main Methods:

  • Utilized a deep learning-assisted generative model for multiobjective optimization.
  • Expanded the dataset using molecular categorization derivation, increasing data points significantly.
  • Employed an asynchronous limited decoder and adversarial regulation for generative efficiency.

Main Results:

  • Achieved 100% generative efficiency for complex molecules in a vast chemical space.
  • Discovered DFEPN, a novel additive with excellent flame resistance and stable interphases.
  • DFEPN demonstrated an order of magnitude increase in capacity retention in Li||LiFePO4 cells, outperforming existing additives.

Conclusions:

  • The developed deep learning approach offers a pathway for designing safe and reliable lithium battery electrolytes, even with limited data.
  • This method has broader implications for the design of advanced batteries.
  • DFEPN represents a significant advancement in flame-retardant electrolyte additives for LMBs.