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<p>Modern battery technologies demand electrolytes that simultaneously deliver multiple functions tailored to diverse applications. Achieving such multi-objective optimization remains fundamentally challenging, as many key electrolyte properties are intrinsically in competition. Data-driven approaches provide a systematic route to navigating the vast compositional space of electrolyte systems; however, their effectiveness critically depends on the availability of comprehensive datasets that capture not only descriptors derived from isolated molecular structures and properties but also structural and physicochemical characteristics of electrolytes as integrated solutions. Here, we report an open electrolyte database comprising approximately 5600 electrolyte formulations, generated using a fully automated, high-throughput molecular dynamics simulation framework. The dataset spans diverse combinations of solvents, salts, and concentrations, and provides unified descriptions of electrolyte structures and physicochemical properties. To facilitate data exploration and utilization, we implement a web-based graphical user interface (<a href="https://oedb.jp">https://oedb.jp</a>) that enables interactive browsing and comparison of electrolyte compositions together with their associated descriptors, while also making the database accessible to LLM-based agents for data reference. This Open Electrolyte Database for Batteries (OEDB) establishes a foundation for data-driven electrolyte design grounded in structure–property relationships at the electrolyte level.</p>

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Open electrolyte database generated via an automated molecular dynamics simulation framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月20日

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  • Kou Nakamura,
  • Norio Takenaka,
  • Masatoshi Hanai,
  • Yuna Oikawa,
  • Ryo Tamura,
  • Koji Tsuda,
  • Masanobu Nakayama,
  • Junichiro Shiomi,
  • Atsuo Yamada

摘要

Modern battery technologies demand electrolytes that simultaneously deliver multiple functions tailored to diverse applications. Achieving such multi-objective optimization remains fundamentally challenging, as many key electrolyte properties are intrinsically in competition. Data-driven approaches provide a systematic route to navigating the vast compositional space of electrolyte systems; however, their effectiveness critically depends on the availability of comprehensive datasets that capture not only descriptors derived from isolated molecular structures and properties but also structural and physicochemical characteristics of electrolytes as integrated solutions. Here, we report an open electrolyte database comprising approximately 5600 electrolyte formulations, generated using a fully automated, high-throughput molecular dynamics simulation framework. The dataset spans diverse combinations of solvents, salts, and concentrations, and provides unified descriptions of electrolyte structures and physicochemical properties. To facilitate data exploration and utilization, we implement a web-based graphical user interface (https://oedb.jp) that enables interactive browsing and comparison of electrolyte compositions together with their associated descriptors, while also making the database accessible to LLM-based agents for data reference. This Open Electrolyte Database for Batteries (OEDB) establishes a foundation for data-driven electrolyte design grounded in structure–property relationships at the electrolyte level.