npj Computational Materials

Predicting accurate cathode properties of layered oxide materials using the SCAN meta-GGA density functional（使用SCAN meta-GGA密度泛函精确预测层状氧化物阴极材料的性质）
Arup Chakraborty, Mudit Dixit, Doron Aurbach & Dan T. Major
npj Computational Materials 4:60 (2018)
doi:s41524-018-0117-4
Published online:8 November 2018
Abstract| Full Text | PDF OPEN

摘要:层状锂嵌入过渡金属氧化物，是有望用于锂离子电池的阴极材料。本研究仔细梳理了最近开发的强约束和适当规范（SCAN）密度泛函方法，以此研究了原型阴极材料LiNiO₂、LiCoO₂和LiMnO₂在不同锂嵌入极限下的结构、磁性和电化学性质。我们的研究表明，SCAN优于早期流行的泛函组合，在不使用Hubbard参数情况下，能得到与实验更为一致的结果，且受色散校正的影响很小。特别地，SCAN比Perdew-Burke-Ernzerhof（PBE）泛函能更好地预测带隙和绝对电压，优于PBE + U预测电子态密度和电压分布，也优于PBE和PBE + U预测电子密度和原位晶格常数。SCAN整体性能表现更好，可归因于局域态处理的改善和短程色散相互作用的更好描述。　　

Abstract:Layered lithium intercalating transition metal oxides are promising cathode materials for Li-ion batteries. Here, we scrutinize the recently developed strongly constrained and appropriately normed (SCAN) density functional method to study structural, magnetic, and electrochemical properties of prototype cathode materials LiNiO₂, LiCoO₂, and LiMnO₂ at different Li-intercalation limits.We show that SCAN outperforms earlier popular functional combinations, providing results in considerably better agreement with experiment without the use of Hubbard parameters, and dispersion corrections are found to have a small effect.In particular, SCAN fares better than Perdew–Burke–Ernzerhof (PBE) functional for the prediction of band-gaps and absolute voltages, better than PBE+U for the electronic density of states and voltage profiles, and better than both PBE and PBE+U for electron densities and in operando lattice parameters.This overall better performance of SCAN may be ascribed to improved treatment of localized states and a better description of short-range dispersion interactions.

Editorial Summary

Batteries: predicting cathodes（电池：预测阴极）

各行各业对能源储存的需求不断增长。锂离子电池被视为满足这些需求的最有前途的技术选择之一。限制现代锂离子电池能量存储的关键因素，是阴极的电化学活性材料。现在来自以色列Bar-Ilan大学的Dan Major等，采用理论方法研究了三种原型层状阴极材料LiNiO2、LiCoO2和LiMnO2在不同Li嵌入极限下的结构细节、带隙、磁性、电子结构，以及形成能。所采用的强约束和适当规范泛函，提供了与大多数属性研究中的可用实验数据更为一致的结果，并证明强约束和适当规范泛函可作为精确预测材料性质的通用方法。

There is an ever-growing demand for energy storage across a wide range of industries and markets. Lithium-ion batteries are viewed as one of the most promising technology choices to meet these needs.A key factor in limiting the amount of energy stored in modern lithium-ion batteries is the electrochemical active material in the cathode.Now, Dan Major and colleagues from Bar-Ilan University in Israel take a theoretical approach to investigate the structural details, band-gap, magnetic and electronic structure, and formation energy of three prototype layered cathode materials LiNiO2, LiCoO2, and LiMnO2at different Li-intercalation limits.The deployed strongly constrained and appropriately normed functional offers better agreement with available experimental data for most studied properties, and demonstrate itself to be a versatile method in materials computation for accurate property prediction.