Li Transport in Defected LiNiO<sub>2</sub> from First Principles: Diffusion, Nucleation, and Charge Transfer
Bibliographic record
Abstract
Electrochemical intercalation of Li in battery electrodes involves a few types of kinetic processes, including diffusion in inhomogeneous media, phase nucleation/transition, and surface charge transfer. They span several length and time scales and are often coupled with each other, which makes it challenging to identify the rate-limiting step and extract kinetic parameters by fitting an empirical model. Using layered cathode LiNiO2 as an example, I will present how all these processes can be incorporated in one atomistic model without empirical inputs, which reals the kinetic competition between different processes. In this scheme, the energetics of various local environments were calculated by the density functional theory (DFT). And the obtained data were used to train a surrogate Hamiltonian with the cluster expansion method. Then the kinetic Monte Carlo (KMC) simulations were conducted with ion hopping barriers updated on-the-fly based on the Brønsted−Evans−Polanyi (BEP) relation. The charge transfer barriers at the electrode-electrolyte interface were calculated separately using the constant-voltage eNEB method1. The simulation results shown below largely reproduce the experimental voltage profile of LiNiO2 2. Both the hysteresis and the irreversible capacity loss (IRC) at the first discharge are captured. The rate limiting step switches as the Li concentration changes during cycling. Moreover, the layered LiNiO2 is never perfect and there are always some excess Ni in the Li layer, which significantly affects the electrochemical performances. My simulations peek into the atomistic origins of these effects. The excess Ni formed in the bulk during synthesis increase IRC by impeding Li diffusion3; the excess Ni near the surface formed during cycling due to oxygen loss traps Li in the fatigued particles by suppressing Li-poor phase nucleation below certain Li concentration4. References: Duan, Z. & Xiao, P. Simulation of Potential-Dependent Activation Energies in Electrocatalysis: Mechanism of O-O Bond Formation on RuO2. Journal of Physical Chemistry C 125, 15243–15250 (2021). Kurzhals, P. et al. The LiNiO2 Cathode Active Material: A Comprehensive Study of Calcination Conditions and their Correlation with Physicochemical Properties. Part I. Structural Chemistry. J Electrochem Soc 168, 110518 (2021). Phattharasupakun, N. et al. Correlating Cation Mixing with Li Kinetics: Electrochemical and Li Diffusion Measurements on Li-Deficient LiNiO2 and Li-Excess LiNi0.5Mn0.5O2. J Electrochem Soc 168, 090535 (2021). Xu, C. et al. Bulk fatigue induced by surface reconstruction in layered Ni-rich cathodes for Li-ion batteries. Nat Mater 20, 84–92 (2021). Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".