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可解读的机器学习加速对/交换在丰富的多层氧化物阴极中兴奋剂效应的分析

Yining Jia¹, Ruiqi Zhang¹, Chi Fang¹

¹School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, People's Republic of China.

The journal of physical chemistry letters

|February 8, 2024

概括

使用特定元素的多层氧化物阴极可以减少不必要的Li/Ni交换缺陷. 机器学习确定了兴奋剂元素的价值和体积变化是影响电池材料中这些缺陷的关键因素.

科学领域:

材料科学材料科学材料科学
计算化学计算化学
电化学电化学电化学

背景情况:

富含的分层氧化物阴极对于高能量密度电池至关重要.
/交换缺陷会对阴极性能和稳定性产生负面影响.
控制/交换是优化阴极材料的关键.

研究的目的:

为了研究兴奋剂元素 (X) 对Li/Ni交换缺陷在LiNi$_{0.8}$Mn$_{0.1}$X$_{0.1}$O$_{2}$的影响.
开发一种可解释的机器学习 (ML) 模型,以确定影响/交换缺陷形成能量的因素.
为评估超级交换相互作用及其对缺陷的影响提出指标.

主要方法:

使用第一原理计算来计算/交换缺陷的形成能量.
使用一种结合随机森林 (RF) 和沙普利增量解释 (SHAP) 的可解释的ML方法.
分析的重点是兴奋剂元素特性,体积变化和缺陷形成能量之间的相关性.

主要成果:

兴奋剂元素的价值状态通过改变Ni价值和超交换相互作用来显著调节Li/Ni交换.
确定COOP_{SU-SD}$和Mag_{O}$是评估超级交换相互作用强度的有效指标.
由于兴奋剂的较大体积减少与/交换缺陷的减少有关.

结论:

兴奋剂策略可以有效地缓解/交换缺陷的丰富的多层氧化物.
该ML模型提供了关于兴奋剂效应对缺陷形成的复杂相互作用的见解.
了解价值状态和体积变化的作用,可以合理设计改进的阴极材料.

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