Article Cite This: J. Phys. Chem. C XXXX, XXX, XXX−XXX
pubs.acs.org/JPCC
Li(Ni,Co,Al)O2 Cathode Delithiation: A Combination of Topological Analysis, Density Functional Theory, Neutron Diffraction, and Machine Learning Techniques Roman A. Eremin,*,† Pavel N. Zolotarev,† Olga Yu. Ivanshina,‡,§,∥ and Ivan A. Bobrikov‡,§ †
Samara Center for Theoretical Materials Science, Samara University, 1 Ac. Pavlova Street, Samara 443011, Russia Joint Institute for Nuclear Research, 6 Joliot-Curie Street, Dubna 141980, Russia § Saratov State University, 83 Astrakhanskaya Street, Saratov 410012, Russia ∥ Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 1(2) Leninskie gory, Moscow (GSP-1) 119991, Russia ‡
ABSTRACT: Here we have combined topological analysis, density functional theory (DFT) modeling, operando neutron diffraction, and machine learning algorithms within the comparative analysis of the known widely LiNiO2 (LNO) and LiNi0.8Co0.15Al0.05O2 (NCA) cathode materials. Full configurational spaces of the mentioned materials during delithiation were set using the topological approach starting from the 2 × 2 × 1 supercell (12 formula units in total) of the LNO structure (space group R3̅m). Several types of the DFT models were applied for the structural relaxation of entries of the LNO configurational space (87 configurations) demonstrating a strong dependence of the results of optimization on the initial structure guess (at the latter delithiation stages) and on the Hubbard correction application (for the whole range of delithiation). Within the computationally easiest model considered for LNO, subsequent modeling of the NCA configurational space (20760 configurations) results in structural changes of the model cell that are well-consistent (relative errors
