Data for the paper "Unlocking a shortcut: Acceleration of non-aqueous electrolyte development by active machine learning for sodium-based batteries"
收藏Zenodo2026-03-23 更新2026-05-26 收录
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https://zenodo.org/doi/10.5281/zenodo.18429012
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资源简介：
Experimental and numerical data collected and analyzed within the paper "Unlocking a shortcut: Acceleration of non-aqueous electrolyte development by active machine learning for sodium-based batteries".
The data include experimental ionic conductivities for Na-based and Li-based non-aqueous electrolytes, galvanostatic cycling performance for optimized Na-based electrolyte formulations and synthetic data to compare different active learning algorithms.
提供机构：
Zenodo
创建时间：
2026-03-23
