Assessment of different loss functions for fitting equivalent circuit models to electrochemical impedance spectroscopy data
This work addresses the need for efficient and accurate loss functions in electrochemical data analysis, particularly for large-scale applications like training machine learning models, though it is incremental as it compares existing and new functions.
The paper tackled the problem of fitting equivalent circuit models to electrochemical impedance spectroscopy data by evaluating new and existing loss functions, finding that the X2 loss function achieved the highest quality of fit, while log-B offered a slightly lower fit but was 1.4 times faster with lower error for most components.
Electrochemical impedance spectroscopy (EIS) data is typically modeled using an equivalent circuit model (ECM), with parameters obtained by minimizing a loss function via nonlinear least squares fitting. This paper introduces two new loss functions, log-B and log-BW, derived from the Bode representation of EIS. Using a large dataset of generated EIS data, the performance of proposed loss functions was evaluated alongside existing ones in terms of R2 scores, chi-squared, computational efficiency, and the mean absolute percentage error (MAPE) between the predicted component values and the original values. Statistical comparisons revealed that the choice of loss function impacts convergence, computational efficiency, quality of fit, and MAPE. Our analysis showed that X2 loss function (squared sum of residuals with proportional weighting) achieved the highest performance across multiple quality of fit metrics, making it the preferred choice when the quality of fit is the primary goal. On the other hand, log-B offered a slightly lower quality of fit while being approximately 1.4 times faster and producing lower MAPE for most circuit components, making log-B as a strong alternative. This is a critical factor for large-scale least squares fitting in data-driven applications, such as training machine learning models on extensive datasets or iterations.