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机器学习和原子尺度机械洞察力,用于设计渐变孔隙MOF衍生的碳电极

Gaoshan Zeng¹, Xuanyi Chen¹, Silu Wang¹

¹College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an 625014, PR China.

Inorganic chemistry

|March 12, 2026

概括

机器学习和DFT优化了超级电容器的多孔碳. 优化的孔径 (1.2 nm微/2.8 nm中) 增强了下一代电极的能量储存和离子传输.

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
计算化学计算化学

背景情况:

金属有机框架 (MOF) 衍生的异构原子合的多孔碳对超级电容电极有很大的希望.
由于孔隙几何学,组成和电化学性能之间的复杂关系,优化具有挑战性.

研究的目的:

为超级电容器应用开发一个可预测的设计策略,以优化MOF衍生的多孔碳.
为了阐明结构-性能关系,控制电化学行为.

主要方法:

将数据驱动的机器学习 (ML) 框架与密度函数理论 (DFT) 集成.
开发一个等级组合模型 (梯度提升和高斯过程回归) 以实现高预测准确度.
使用DFT和分子动力学进行原子尺度模拟,以调查O和Co共碳中的机制.

主要成果:

该ML模型实现了高预测准确性 (测试R2 = 0.99),识别了最佳的微/半孔结构 (∼1.2nm微孔,∼2.8nm半孔).
发现C-O-Co杂交和氧气诱导的富电子位点可以增强界面极化和离子电极相互作用.
模拟显示了吸附能量梯度,促进了离子迁移,改善了储存密度和运输动力学.

结论:

为设计高性能超级电容电极,建立了一个数据驱动的,可解释的框架.
优化的孔隙架构对于平衡电荷储存和离子传输至关重要.
这种方法使下一代储能材料的合理设计成为可能.

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