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Machine learning-assisted discovery of outside-in structure Ni-rich cathode with high performance.

Guihong Mao1, Ying Wang2, Tengyu Yao1

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Researchers developed a new Ni-rich cathode for lithium-ion batteries using machine learning. This advanced material shows improved stability and retains high capacity over 200 cycles, reducing degradation issues.

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Battery Technology

Background:

  • Nickel-rich (Ni-rich) oxides are promising cathode materials for lithium-ion batteries due to their high energy density, cost-effectiveness, and sustainability.
  • However, Ni-rich cathodes face challenges like voltage fade and capacity degradation, primarily caused by lattice strain and surface reconstruction.

Purpose of the Study:

  • To engineer a high-performance Ni-rich cathode with enhanced stability and longevity.
  • To address the issues of interfacial degradation and anisotropic lattice strain in Ni-rich cathode materials.

Main Methods:

  • Utilized machine learning to identify suitable dopants (Al³⁺ and Sn⁴⁺) for creating a robust 'outside-in' architecture.
  • Employed a competitive doping strategy to form a stable surface layer and a uniformly doped bulk structure.

Main Results:

  • The developed cathode exhibited a unique Sn-rich rock-salt surface layer and an Al-doped bulk, enhancing interfacial stability.
  • The structure effectively mitigated cathode/electrolyte degradation and reduced anisotropic lattice strain, suppressing Li-Ni disorder.
  • The Ni-rich cathode demonstrated excellent performance, retaining 96.9% of its capacity after 200 cycles with minimal voltage fade.

Conclusions:

  • The 'outside-in' architecture, achieved through machine learning-guided doping, significantly improves the structural reversibility and electrochemical stability of Ni-rich cathodes.
  • This approach offers a viable strategy for developing next-generation, high-performance, and durable cathode materials for advanced lithium-ion batteries.