A Convolutional Neural Network to Predict the State-of-Lithiation of Lithium-ion Batteries with the Nickel-Manganese-Cobalt-Oxide Chemistry
Bibliographic record
Abstract
A machine learning (ML) model was developed to study the discharge behavior of a 𝐿𝑖x𝑁𝑖0.33𝑀𝑛0.33𝐶𝑜0.33𝑂2 half-cell with particle-scale resolution. The ML model could predict the state-of-lithiation of the particles as a function of time and C-rate. Although direct numerical simulation has been well established in this area as the prevalent method of modeling batteries, computational expense increases going from 1D-homogenized model to particle-resolved models. The model was trained on a total of sixty different electrodes with various lengths for a total of 4 different C-rates: 0.25, 1, 2, and 3C. The ML model uses convolutional layers, resulting in an image-to-image regression network. To evaluate model performance, the root mean squared error was compared between the state of lithiation (SoL) predicted by the ML model and ground truth results from pore-scale direct numerical simulation (DNS) on unseen electrode configurations. It was shown that the ML model can predict the SoL within 3.76% accuracy in terms of relative error, but almost an order of magnitude faster than the DNS approach.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".