Screening the Li-Ni-Mn-Co-O Composition Space in High-Throughput for Li-Ion Batteries
Bibliographic record
Abstract
Currently, layered metal oxide materials are the norm in manufactured cathodes for Li-ion batteries, with LiNixMnyCo1-x-yO2 (NMC) being one of the leading class of materials. While NMC has been extensively studied, the composition space for the material has not been entirely explored, especially when one includes Li-rich compositions. Screening entire pseudo-quaternary systems is extremely time-intensive such that high-throughput techniques are necessary in order to enable study of the complex composition spaces. Herein, synthesis and characterization of a large number (several hundreds) of samples is accomplished by performing all work in high-throughput (sets of 64 samples of varying metal concentration were synthesized simultaneously). All samples were characterized structurally using a high-throughput X-ray diffractometer capable of acquiring 64 Rietveld quality patterns on mg-scale samples in 12 h. The XRD patterns were analyzed to generate structural phase diagrams as shown for Li-Mn-Ni-O below. The structural studies are coupled with high-throughput electrochemistry recently developed in the lab. Cyclic voltammetry was performed on all samples to gain insight into their performance as a cathode material. The results for energy density are shown in the figure below. Results for the quaternary system including various concentrations of Co will also be presented. This work marks the beginning of the thorough structural/electrochemical mapping of many layered oxide composition spaces and will enable meaningful structure-property relations that will guide the rational design of new cathode materials for Li-ion cells. 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".