Autoeis: Automated Bayesian Model Selection and Analysis for Electrochemical Impedance Spectroscopy
Bibliographic record
Abstract
Electrochemical impedance spectroscopy (EIS) is a cornerstone analysis technique for understanding the complex transport processes occurring in electrochemical systems. However, traditional methods of analyzing EIS data such as equivalent circuit models (ECMs) rely on experts’ tacit knowledge and thus are prone to subjectivity. This can lead to inconsistent and biased analysis and physical interpretations. To mitigate these concerns, we developed AutoEIS a tool that combines evolutionary algorithms and Bayesian Inference to automatically proposing statistically plausible equivalent circuit models. AutoEIS does this without requiring curated training data and thus can be applied to any electrochemical system in a way that minimizes bias in EIS analysis. AutoEIS firstly identifies a broad range of candidate ECMs that fit the EIS measurement via an evolutionary algorithms-based exploration. It then evaluates the appropriateness of each ECM based on the information contained in the EIS using Bayesian Inference. These processes allow AutoEIS to detect potential misalignments in EIS-ECM pairs. Such mismatches occur when a given ECM appears to fit well based on standard metrics and intuition but lacks credibility due to insufficient information contained in the EIS data. Here we validate AutoEIS’s ability to identify statistically plausible ECMs across various electrochemical scenarios, ranging from relatively simple systems like oxygen evolution reaction to more complex systems such as carbon dioxide reduction. We further demonstrate that this framework can be used to determine the minimal frequency required to be measured for maintaining the integrity of EIS analysis. It points to a pathway for expediting EIS acquisition by intelligently streamlining low-frequency EIS collection. These results highlight AutoEIS’s potential in both in-situ and high-throughput EIS analysis by enabling more efficient data acquisition while preserving analysis reliability.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".