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通过混合机器学习优化对离子电池阴极的6858种化合物的高通量选

Yakubu Sani Wudil^1,2, Mohammed A Gondal^2,3, Mohammed A Al-Osta^1,4

¹Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.

ACS applied materials & interfaces

|February 4, 2025

概括

机器学习加速了用于离子电池的正极材料的发现. 该框架通过预测关键性质和应用对增强能源存储的选标准,确定了18个有前途的电极.

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:

离子电池 (ZIB) 为离子电池提供了一个可持续的替代品.
发现高效和稳定的阴极材料对于ZIB的发展至关重要.
需要数据驱动的方法来加速材料的发现.

研究的目的:

开发一个机器学习 (ML) 框架,用于探索ZIBs的新型阴极材料.
预测电化学性质并从大数据集中选潜在的候选物.
为了加快识别高性能和稳定的ZIB阴极材料.

主要方法:

利用了来自材料项目 (MP) 数据库的6858种含化合物的数据集.
采用双步ML方法与转移学习来归因缺失的电化学数据.
通过主要组件分析进行特征减少后开发出混合模型 (SSA-LGBM和HHO-DNN).

主要成果:

成功预测了62个潜在电极的平均电压和重力学容量等关键性质.
根据电压,容量,导电性,安全性,稳定性,成本和丰富性来选这些候选者.
确定了18种用于离子电池应用的有希望的阴极材料.

结论:

ML框架显著加快了ZIB的高效和稳定的阴极材料的发现.
这种方法为各种电池技术的未来材料勘探提供了可靠的方法.
能够提供更可持续和高性能的储能解决方案.

关键词:

电极的发现电极的发现储能储能是一种储能.机器学习是机器学习.材料勘探材料勘探材料勘探可持续性可持续性可持续性离子电池离子电池

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