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Advancing Lithium–Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence-Driven Design Paradigms

  • Yuqing Yao
  • , Chongyang Hao
  • , Karol Viviana Mejia-Centeno
  • , Malik Dilshad Khan
  • , Shang Wang
  • , Longqiu Li
  • , Yanhong Tian
  • , Andreu Cabot
  • , Qing Sun*
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Catalonia Institute for Energy Research
  • University of Barcelona
  • Zhejiang University
  • ICREA

Research output: Contribution to journalReview articlepeer-review

Abstract

Lithium-oxygen batteries (LOBs) are regarded as one of the most promising next-generation energy storage systems, owing to their exceptionally high theoretical energy density. However, their practical application remains severely hindered by sluggish oxygen reduction and evolution reaction kinetics, insulating discharge products, large voltage polarization, and poor cycling stability. Cathode catalysts play a pivotal role in regulating reaction pathways, accelerating interfacial kinetics, and improving reaction reversibility. This review systematically summarizes the fundamental working principles and key challenges of nonaqueous LOBs, followed by a comprehensive overview of recent advances in cathode catalyst materials, including carbon-based catalysts, noble metals, transition metal oxides, sulfides, nitrides, carbides, and redox mediators. Particular emphasis is placed on emerging catalyst design strategies, such as single-atom catalysts, metal–organic frameworks-derived materials, heterostructure and interfacial engineering, high-entropy catalysts, and spin-related oxygen electrocatalysis. In addition, recent progress in high-throughput computation and artificial intelligence-driven catalyst design is explored, highlighting the potential for data-assisted discovery, multiscale structure-activity relationships, and mechanism-guided optimization. The review concludes by addressing the critical challenges and outlining future directions for the development of efficient, durable, and practically viable cathode catalysts for nonaqueous LOBs.

Original languageEnglish
Article numbere73460
JournalAdvanced Materials
Volume38
Issue number38
DOIs
StatePublished - 8 Jul 2026

Keywords

  • artificial intelligence
  • catalyst
  • high throughput
  • lithium–oxygen batteries
  • machine learning
  • oxygen evolution reaction
  • oxygen reduction reaction

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