CALiSol-23:各种盐和溶剂组合的实验电解质导电性数据
Paolo de Blasio1, Jonas Elsborg1, Tejs Vegge1
1Technical University of Denmark, Department of Energy Conversion and Storage, Kgs. Lyngby, 2800, Denmark.
Scientific data
|July 10, 2024
概括
一个新的数据集,CALiSol-23,收集了非水性电解质的实验性离子导电率数据. 该资源有助于开发机器学习模型,以更快地发现先进的离子电池电解质.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 数据科学数据科学数据科学
背景情况:
- 高性能离子电池 (LIB) 取决于非水性电解质中的有效离子运输.
- 发现最佳的电解质成分需要广泛的,耗时的实验.
- 对于全面的数据集存在关键需求,以实现电解质设计中的数据驱动方法.
研究的目的:
- 整理和分析一个大规模的数据集,实验测量了非水性电解质的离子导电率.
- 支持开发化学不可知的机器学习模型,用于预测电解质导电性.
- 加速发现和优化LIBs的新型电解质配方.
主要方法:
- 进行了详尽的文献审查,以收集关于非水性电解质导电性的实验数据.
- 编制了一个名为CALiSol-23的数据集,包括来自27篇研究文章的13825个数据点.
- 该数据集涵盖38种不同的溶剂,14种盐和广泛的温度范围.
主要成果:
- CALiSol-23数据集为各种电解质系统的离子导电性提供了全面的资源.
- 它包括有关盐,溶剂,度和温度变化的详细信息.
- 该数据集有助于分析离子运输机制和电解质性能因素.
结论:
- CALiSol-23解决了对基于机器学习的电解质发现至关重要的全面数据集的缺乏.
- 这种资源可以显著加快电解质导电性的预测模型的开发.
- 它将简化非水性电解质混合物的优化,用于先进的储能应用.
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