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<p>In this study, we present a large-scale machine learning screening to discover promising candidate compounds for lithium-based solid-state electrolyte batteries. Key properties such as superionic conductivity and wide electrochemical stability are crucial for achieving high-performance solid-state batteries, which have great potential as the next generation of batteries with high energy density and relatively low cost. Our work employs high-throughput screening using multiple regression machine learning models on lithium-containing materials. Subsequently, <i>ab initio</i> molecular dynamics (AIMD) simulation and experimental validation were conducted exhibiting high ionic conductivity, namely Li<sub>4.5</sub>TiO<sub>3.25</sub> and Li<sub>2</sub>VCl<sub>5</sub>. Furthermore, we applied a design methodology to increase the ionic conductivity at ambient temperature. These findings provide a comprehensive strategy for the development of room-temperature superionic conductors for high-performance solid-state batteries.</p>

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Accelerated discovery of solid-state battery properties enabled by active learning approaches

  • 期刊论文

  • 发表日期：

    2025年5月12日

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  • Mohamed Ait Tamerd,
  • Xiaoting Lin,
  • Ji’an Wang,
  • Limin Cai,
  • Abdelilah Lahmar,
  • Jiwei Ma,
  • Menghao Yang

摘要

In this study, we present a large-scale machine learning screening to discover promising candidate compounds for lithium-based solid-state electrolyte batteries. Key properties such as superionic conductivity and wide electrochemical stability are crucial for achieving high-performance solid-state batteries, which have great potential as the next generation of batteries with high energy density and relatively low cost. Our work employs high-throughput screening using multiple regression machine learning models on lithium-containing materials. Subsequently, ab initio molecular dynamics (AIMD) simulation and experimental validation were conducted exhibiting high ionic conductivity, namely Li4.5TiO3.25 and Li2VCl5. Furthermore, we applied a design methodology to increase the ionic conductivity at ambient temperature. These findings provide a comprehensive strategy for the development of room-temperature superionic conductors for high-performance solid-state batteries.