通过机器学习引导的实验发现金属有机框架的双离子-电子导电性
Robabeh Bashiri1, Preston S Lawson1, Stewart He2
1Department of Chemistry and Biochemistry, California State University, Chico, California 95929-0210, United States.
Chemistry of materials : a publication of the American Chemical Society
|February 17, 2025
概括
机器学习快速选金属有机框架 (MOFs) 的离子电子导电性,确定了两个有前途的p型MOFs. 这些地球上丰富的材料为设计先进的储能解决方案提供了洞察力.
科学领域:
- 材料科学 材料科学 材料科学
- 化学 化学 化学
- 储能 储能 储能 储能 储能 储能
背景情况:
- 具有合离子-电子行为的导电金属有机框架 (MOF) 对于储能至关重要.
- 选这些属性的广的MOF景观是具有挑战性的.
- 机器学习 (ML) 提供了一种快速且具有成本效益的方法来识别候选MOF.
研究的目的:
- 使用ML和实验验证来识别具有合离子-电子导电性的MOF.
- 为了揭示结构-属性关系,支配MOF导电性.
- 为指导用于储能的地球丰富MOF的设计.
主要方法:
- 用分类和回归机器学习模型来选COREMOF数据库.
- 在ML预测导电MOF上进行实验验证.
- 进行了电化学表征和深入的结构分析.
主要成果:
- 两个p型导电MOFs,[Cu3(μ3-OH) ((μ3-C4H2N2O2) 3 ((H3O) ]·2C2H5OH·4H2O (1) 和NH4[Cu3(μ3-OH) ((μ3-C4H2N2O2) 3) ·8H2O (2),已被验证为合的离子-电子行为.
- 结构-属性关系显示,O-Cu-N链和特定联结体增强导电性.
- 较高的离子导电性与通过链接器或氨离子存在的离子流动性有关.
结论:
- 结合实验验证的ML选对于发现导电MOF是有效的.
- 地球上丰富的铜和基于Pyrazole的MOF显示了储能的巨大潜力.
- 了解结构-属性关系是设计下一代用于离子-电子导电性的MOF的关键.
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