Metal Release Mechanism and Electrochemical Properties of Lix(Ni1/3Mn1/3Co1/3)O2
Metal Release Mechanism and Electrochemical Properties of Lix(Ni1/3Mn1/3Co1/3)O2
复制标题
DOI：
10.3390/app12084065
复制
发表时间：
2022-04
期刊：
影响因子：
--
通讯作者：
Blake G. Hudson;S. Mason
中科院分区：
文献类型：
--
作者：
Blake G. Hudson;S. Mason
复合金属氧化物（CMO）广泛用于包括锂离子电池技术中发现的电反应形式的应用中。计算化学可以提供关于CMO阴极材料的性质如何响应于化学计量的变化（例如，在电池的充电-放电循环期间锂（Li）含量的变化）而变化的独特信息。然而，由于阳离子的横截面积很小，这很难通过实验测量。在操作条件之外，当暴露于环境时，Li含量可影响CMO的转变。例如，已确定含水环境中CMO的金属释放是CMO降解的重要交叉机制。研究从CMO释放金属的计算研究表明，热力学依赖于晶格阳离子的氧化态，这预计将随锂含量而变化。在这项工作中，计算研究跟踪金属释放趋势的变化作为锂含量的函数在Lix（Ni 1/3 Mn 1/3Co 1/3）O2（NMC）。由此产生的数据集用于构建随机森林树（RFT）机器学习（ML）模型。脱锂研究中的建模挑战是要采样的大构型空间。通过研究每个锂馏分的多种构型，我们发现了与有利能量相关的结构特征，以化学方式指导相关结构的识别并充分预测电压值。
Complex metal oxides (CMOs) are used broadly in applications including electroreactive forms found in lithium-ion battery technology. Computational chemistry can provide unique information about how the properties of CMO cathode materials change in response to changes in stoichiometry, for example, changes of the lithium (Li) content during the charge–discharge cycle of the battery. However, this is difficult to measure experimentally due to the small cross-sectional area of the cations. Outside of operational conditions, the Li content can influence the transformations of the CMO when exposed to the environment. For example, metal release from CMOs in aqueous settings has been identified as a cross-cutting mechanism important to CMO degradation. Computational studies investigating metal release from CMOs show that the thermodynamics depend on the oxidation states of lattice cations, which is expected to vary with the lithium content. In this work, computational studies track changes in metal release trends as a function of Li content in Lix(Ni1/3Mn1/3Co1/3)O2 (NMC). The resulting dataset is used to construct a random forest tree (RFT) machine learning (ML) model. A modeling challenge in delithiation studies is the large configurational space to sample. Through investigating multiple configurations at each lithium fraction, we find structural features associated with favorable energies to chemically guide the identification of relevant structures and adequately predict voltage values.