一个集成的高通量机器人平台和积极学习方法,以加速发现最佳电解质配方
Juran Noh1, Hieu A Doan2, Heather Job1
1Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
Nature communications
|March 30, 2024
概括
这项研究开发了一种使用机器学习和高通量实验的自动化工作流程,以寻找氧化还原活性分子的溶剂. 该方法有效地识别了用于氧化还原流电池的高可溶性有机溶剂.
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
- 化学工程是化学工程的重要组成部分.
背景情况:
- 氧化还原活性分子的可溶性对氧化还原流电池能量密度至关重要.
- 有限的实验溶解度数据阻碍了电解质材料的数据驱动发现.
研究的目的:
- 设计和研究一种高度自动化的工作流程,以提高氧化还原活性分子的可溶性.
- 为了加速新型电解质材料的发现,用于高性能氧化还原流电池.
主要方法:
- 高通量实验平台与主动学习算法的协同集成.
- 对2000多种潜在有机溶剂进行选,以寻找原型分子2,1,3-二甲.
- 利用机器学习指导来最大限度地减少所需的实验性溶解性评估的数量.
主要成果:
- 识别了多种溶剂,对于2,1,3-二二醇,其可溶性值>6.20M.
- 证明了高效率,需要对<10%的选候选人进行溶解性评估.
- 揭示了二元溶剂混合物,特别是与1,4-二氧化,显著提高溶解度.
结论:
- 开发的机器学习引导,高通量机器人平台为发现高可溶性氧化还原活性材料提供了高效的工作流程.
- 这种方法加速了先进的氧化还原流电池的发展.
- 该方法提出了一个可通用的战略,用于加速功能材料的发现.
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