Amin Sadeghi

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 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. This trade-off is particularly relevant for self-driving laboratories (SDLs), where EIS measurements and ECM fitting are performed autonomously in closed-loop workflows. In such settings, a computationally efficient and robust loss function is essential to prevent error accumulation across iterations and to enable scalable, reliable electrochemical SDLs.

Pore network models are useful for efficiently studying transport processes in porous materials at the pore scale. However, constructing accurate pore network representations is not always straight forward as it involves either complicated image processing or tedious calibration of network properties to experimental data. While network extraction tools are much more accessible today, access to high resolution X-ray tomography images is not nearly as widespread. Researchers of porous materials without access to high resolution microtomography images must therefore rely on calibrating a pore network by manually adjusting pore sizes and constantly checking that it satisfies the properties of the actual material. This paper presents a new strategy for fitting a regular lattice-based pore network to experimental data by using automatic differentiation for gradient descent optimization. The optimization was demonstrated on a data set of carbonate, sandstone, and sandpack materials for which images and experimental information were both available. The optimization of a 103 pore network took on average 21 and a half minutes on a GPU and the resulting networks matched the porosimetry curves and permeability in all directions of the actual material very closely. The optimization was then tested on real experimental data in which a final loss of 9.7 x 10-4 was achieved. In addition, a workflow was developed for obtaining a stochastic network of arbitrary size from the fitted network. This required a description of the highly specific spatial arrangement of pore sizes found by the optimization, for which a Gaussian Process model was trained.