Li/Vacancy Configurations in Surface Region of Li2−X
MnO3 Cathode Material: A High-Throughput Computational Investigation
Authors: Duy Truong Vinh Truong , Ohwaki Tsukuru , Imai Hideto 
Published: February 2020

Li-rich materials Li2MnO3-LiMO2 (M = Co, Ni, Mn) have received a great deal of research attention due to their attractive properties of high reversible capacity and energy density as lithium-ion battery (LIB) cathodes. Unfortunately, the commercialization of the materials has been hindered by numerous technical issues and challenges, notably the lack of clear understanding of the mechanism causing charge and discharge irreversibility. For understanding the mechanism, a lot of effort has been put into examining the irreversible deterioration of Li2MnO3, for instance in [1] by analyzing the effect of the lithium diffusion, the oxygen deficiency, and other factors on structural evolution and charge compensation. However, most previous works mainly focused on bulk cathode materials, while the surface region is also expected to remarkably affect the electrochemical activities of the materials. In this regard, the link between stable Li/vacancy configurations and Li concentration in the surface region is therefore essential for understanding the electrochemical properties and behavior of the surface region during charge and discharge. On the other hand, high-throughput computational materials design has emerged as a powerful and promising approach for the discovery and comprehensive analysis of materials [2]. Examples for LIB research include [3], where 515 stable lithiation reactions of some selected anode materials were enumerated based on a calculated energy data set of 291 compounds. Recently, the relationship between voltage and safety of LIBs was investigated using 1,400 cathode materials in an identical high-throughput fashion [4].

In this work, we exhaustively examine the relationship between electrochemically stable Li/vacancy configurations and their corresponding voltage in the surface region of Li2MnO3 in the solid-state Li2MnO3-LiMO cathode material through high-throughput computing. Figure 1 shows the slab model of Li2MnO3 (010) with the equivalent cell of Li16Mn8O24 used in this study. For each x in Li2−x
MnO3, where 0.0 ≤ x ≤ 2.0 indicating that up to all of 16 Li atoms are subjected to removal, all possible placements of the remaining Li atoms in the lattice are considered, and the total energies of the resulting Li/vacancy structures are calculated, based on which the ground-state Li/vacancy structure is determined. Exploring all possible values of x for up to 16 Li atoms requires 216= 65,536 structures to be calculated and examined. With the aim of enabling the full exploration of such a large search space, we have developed our own software tool for high-throughput density functional theory (DFT) calculations that can effectively and reliably handle concurrent task executions, task and data management, and parameter optimization on a couple of different computing platforms. The calculations are performed by our tool utilizing the OpenMX code [5] as the DFT engine, with the spin-polarized GGA+U scheme having the Hubbard U value of Mn set at 5 eV to account for the strong correlation effect, the PBE functional, an energy cutoff of 300 Ry, and a k-point mesh of 3x3x1.

Figure 2 presents several preliminary results showing the voltage profile with respect to x in the Li2−x
MnO3 (010) surface region. As can be seen from the figure, although the voltage of the Li2−x
MnO3 surface region tends to increase from about 4 V to 5 V with an increase in x where 0.0 ≤ x ≤ 0.5, it converges at around 5 V where 0.5 ≤ x ≤ 2.0. This voltage difference is found to be quite considerable in comparison with that of about 0.05 V in [1], which also observed a similar trend of voltage gain with an increase in x in the bulk Li2-x
MnO3 (0.0 ≤ x ≤ 1.0). As a result, the link between voltage and x in the surface region is far more obvious than that in the bulk when the depth of charge is shallow. We will thoroughly analyze and report our latest findings based on a complete coverage of x in the presentation.

Acknowledgement

The calculations in this work were partly performed using the K computer at the RIKEN Advanced Institute for Computational Science.

References

[1] R. Xiao, H, Li, and L. Chen, Chem. Mater. 24 (21), 4242–4251 (2012).

[2] S. Curtarolo, G. L. W. Hart, M. Buongiorno Nardelli, N. Mingo, S. Sanvito, and O. Levy, Nat. Mater. 12(3), 191-201 (2013).

[3] S. Kirklin, B. Meredig, C. Wolverton, Adv. Energy Mater. 3, 252–262 (2013).

[4] A. Jain, G. Hautier, S. P. Ong, S. Daceka, G. Ceder, Phys. Chem. Chem. Phys. 17, 5942-5953 (2015).

[5] OpenMX: Open source package for Material eXplorer, http://www.openmx-square.org/

Figure 1