Structural Files LRLO antisites
Description
This database consists in the POSCAR files used as input files for DFT calculations to investigate antisite defects in LRLO materials. Li-rich layered oxide (LRLO) materials are extensively studied as promising positive electrode materials for Li-ion batteries. The formation of Li/Ni antisite defects in the layered lattice of the Co-free LRLO Li1.2Mn0.6Ni0.2O2 compound is here investigated. Antisite defects involving transition metals (TMs), preferentially nickel, and lithium ions occurs spontaneously in many LRLO but their impact on the functional properties in batteries is controversial. Here we propose a simple thermodynamic model to evaluate the concentration of antisite defects in the rhombohedral lattice of the Li1.2Mn0.6Ni0.2O2 as a function of temperature. This model is based on defect formation thermodynamics derived from density functional theory (DFT) calculations performed on disordered supercells. Our aim is to shed light on the native disorder in LRLO and how the synthesis protocol can promote the antisite defect formation. Computational Data were acquired using VASP (Vienna Ab inito simulation packaging) within the Kohn-Sham density functional theory (DFT) framework. We used the DFT+U method. Analysed/processed Data
Files
Steps to reproduce
Supercell 5x2x1 = Li36Ni6Mn18O60; INCAR (input file) = Energy cut-off 520 eV, Brillouin Zone sampled at the Gamma point, U values for Ni and Mn = 4 eV; pseudopotential PAW_PBE = Li_sv, Ni, Mn and O; optimization of the structural parameters of supercells by relaxing iteratively the ion positions and the cell lattices without any symmetry constraints until the residual force on each atom was <0.01 eV Å−1 Starting from an hR12 prototype unit cell (R3 ̅m symmetry) a 5x2x1 supercell has been built using the software VESTA and randomized using SQS method and ATAT suite (Alloy-Theoretic Automated Toolkit). DFT+U self-consistent field calculations with the VASP code have been performed at the CRESCO supercomputer centre at ENEA.