Predicting accurate cathode properties of layered oxide materials using the SCAN meta GGA density functional
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.Batteries: predicting cathodesThere 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 LiMnO2 at 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.
npj Computational Materials