including the Gaussian-process-based DRT (GP-DRT) model . However, the GP-DRT method can produce negative DRT values (if a non-negativity constraint is not imposed through some inducing points) and is limited to the imaginary part of the EIS spectrum. To address these challenges, we developed the finite GP-DRT (fGP-DRT) framework , which inherits the key properties of GP-DRT, utilizes both components of the impedance, and enforces a non-negativity constraint on the DRT. Despite the promise, the current implementation of the fGP-DRT framework can only deconvolve one EIS spectrum at a time, limiting the analysis of EIS data to a single experimental state. This limitation could hinder the identification of electrochemical processes across an array of 𝝍 values, which is crucial for assessing how system characteristics shift during experiments.