Analysis of Thousands of Electrochemical Impedance Spectra of Lithium-Ion Cells through a Machine Learning Inverse Model
Bibliographic record
Abstract
Electrochemical impedance spectra of lithium-ion cells can be collected periodically at various cycle numbers and various state of charges, producing vast amounts of data. Fitting each spectrum to an equivalent circuit can lead to physical insights about the evolution of the lithium-ion cell, yet the fitting problem requires good human initial guesses for the circuit parameters to reliably converge, making the fitting process labor intensive and difficult to scale. This article presents a paradigm to automate the fitting of measured data to physical models, replacing the good human first guesses with an inverse model parametrized with an artificial neural network. This method is simple to implement, uses principles applicable to a wide variety of fitting problems, and leads to reliable and accurate initial guesses of the circuit parameters for a given spectrum. The software implementation will be freely available once a good user interface is developed, and the performance of the system is evaluated on a dataset of about 100000 impedance spectra from lithium-ion cells, achieving a failure of fitting approximately 1% of the dataset, corresponding to the percentage of poor quality data in the dataset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".