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释放双离子电池的潜力:通过机器学习识别多循环芳香碳化合物阴极和间隙盐组合

Sandeep Das¹, Souvik Manna¹, Biswarup Pathak¹

¹Department of Chemistry, Indian Institute of Technology (IIT) Indore, Indore, Madhya Pradesh 453552, India.

ACS applied materials & interfaces

|November 16, 2023

概括

机器学习模型使用多环芳碳水化合物阴极准确预测双离子电池的性能. 这加快了稳定,高性能储能解决方案的设计.

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
计算化学的计算化学

背景情况:

双离子电池 (DIBs) 提供具有成本效益和安全的高性能能量存储.
为DIB设计最佳的电极-电解质组合是具有挑战性的,因为配置空间很大.

研究的目的:

开发一种机器学习 (ML) 方法,用于预测DIBs的多环芳 (PAH) 阴极的电压和体积变化.
选多种PAH和DIB盐组合,以提高电池性能和稳定性.

主要方法:

利用在实验数据上训练的梯度增强和XGBoost回归模型.
预测大约700种PAH-DIB盐间组合的电化学特性.
通过密度函数理论 (DFT) 计算验证了一组预测.

主要成果:

机器学习模型在预测电压和体积变化方面表现出很高的准确性.
为了满足特定的电压要求和长期稳定性,确定了首选的PAH-DIB盐组合.
气阴极表现出良好的电化学性能,体积变化低于石墨.

结论:

该研究建立了基于ML的可靠工作流程,用于加速设计先进的PAH阴极和DIB盐.
氧化显示出克服DIBs循环稳定性问题的潜力.
这种方法通过高效的材料选推动了DIB技术的进步.

关键词:

双离子电池双离子电池机器学习是机器学习.聚环芳香碳化合物聚环芳香碳化合物盐的盐是盐,盐的盐是盐.电压预测电压预测变化体积变化体积变化

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