Electrochemical impedance spectroscopy (EIS) is an experimental technique widely used for the analysis of electrochemical energy devices (e.g., batteries , fuel cells , and supercapacitors ), medical , and biological systems. The major appeals of this technique are that it can be carried out over a wide range of frequencies (from mHz to MHz) and that modern equipment allows us to measure EIS spectra quickly and efficiently with limited user expertise. To interpret EIS data, equivalent circuits and physical models are often utilized . However, despite their simplicity, equivalent circuit models (ECMs) are not unique. Physical models can provide more insights, but are problem-specific and significantly more difficult to implement . Considering these challenges, the distribution of relaxation times (DRT) has recently emerged as an alternative approach to analyze EIS data . The DRT model assumes that the electrochemical processes underpinning EIS spectra are relaxations . This means that the DRT impedance model, 𝑍 (𝑓), at a given frequency, 𝑓, is obtained using the following expression 𝑍 (𝑓, 𝛾) = 𝑖2𝜋𝑓𝐿 + 𝑅 + 𝛾(log 𝜏) 1 + 𝑖2𝜋𝑓𝜏 𝑑log 𝜏 where 𝐿 , 𝑅 , 𝜏, and 𝛾(log 𝜏) are the inductance, ohmic resistance, timescale, and DRT, respectively. Usually, the constraint 𝛾(log 𝜏) ≥ 0 is also assumed to avoid negative relaxations . Despite the DRT's considerable promise, deconvolving 𝛾(log 𝜏) from experimental data is still challenging. Indeed, the inverse problem that needs to be solved to obtain 𝛾(log 𝜏)