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<p>The research and development of solid-state electrolytes (SSEs) play a critically important role in fabricating high-energy solid-state batteries (SSBs). Establishing predictive models via machine learning (ML) and high-throughput screening (HTS) can not only reduce time and material costs for developing novel solid-state electrolyte (SSE) materials, but also enable rational design and performance prediction of new solid SSEs. To date, numerous studies have utilized artificial intelligence (AI) tools to explore innovative materials, and SSEs are also in urgent need of development in this way. However, research groups differ in their priorities, starting frameworks, screened candidate materials, and perspectives on predictive development strategies for new SSEs. This review systematically summarizes the critical applications and recent advancements of ML in solid-state battery research. It centers on four core research directions: advances in computational simulation, HTS of materials, interfacial challenges of SSEs, and the integration of artificial intelligence. The review indicates that while ML has significantly advanced key materials research in SSBs, challenges remain, including model reliance on expensive traditional density functional theory (DFT) data, difficulties in integrating high-quality standardized data, and insufficient multi-physics/multi-scale coupling modeling. Future research will focus on deeply integrating LLMs to build intelligent research platforms, developing universal and transferable AI models, enhancing multi-scale simulation and digital twin technologies, and establishing a closed-loop “computation-experiment” optimization paradigm to accelerate the development and practical application of high-performance, high-safety SSBs.</p>

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Advancing machine learning for solid-state electrolytes: from materials screening to key problem resolution

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Jinjian Li,
  • Xuhong Sun,
  • Zhiqiang Yu,
  • Xiaoxiao Lu,
  • Yanwei Huang

摘要

The research and development of solid-state electrolytes (SSEs) play a critically important role in fabricating high-energy solid-state batteries (SSBs). Establishing predictive models via machine learning (ML) and high-throughput screening (HTS) can not only reduce time and material costs for developing novel solid-state electrolyte (SSE) materials, but also enable rational design and performance prediction of new solid SSEs. To date, numerous studies have utilized artificial intelligence (AI) tools to explore innovative materials, and SSEs are also in urgent need of development in this way. However, research groups differ in their priorities, starting frameworks, screened candidate materials, and perspectives on predictive development strategies for new SSEs. This review systematically summarizes the critical applications and recent advancements of ML in solid-state battery research. It centers on four core research directions: advances in computational simulation, HTS of materials, interfacial challenges of SSEs, and the integration of artificial intelligence. The review indicates that while ML has significantly advanced key materials research in SSBs, challenges remain, including model reliance on expensive traditional density functional theory (DFT) data, difficulties in integrating high-quality standardized data, and insufficient multi-physics/multi-scale coupling modeling. Future research will focus on deeply integrating LLMs to build intelligent research platforms, developing universal and transferable AI models, enhancing multi-scale simulation and digital twin technologies, and establishing a closed-loop “computation-experiment” optimization paradigm to accelerate the development and practical application of high-performance, high-safety SSBs.