( Next, we used the DNNs described in Section 3 to deconvolve the DCT, which is shown in Figure (c) with the exact DCT. We note that the exact DCT was accurately recovered, which is consistent with the residuals between the "experimental" and estimated admittances that converge after approximately 4.00×10 4 iterations (Figure (a) of SI2). Moreover, we used a modified version of the peak-separation algorithm of pyDRTtools based on to separate the peak of the DNN-regressed DCT (Section 3). The peak separation enabled us to identify the characteristic timescales Notably, the exact impedance and admittance were closely regressed. However, we observed a slight mismatch between the exact and regressed impedances at low frequencies. Although the exact admittance was accurately regressed over the complete range of probed frequencies, this deviation arose from taking the inverse of the estimated admittances. In fact, applying the inverse intensifies the difference between regressed and "experimental" data. Another reason for this mismatch at low frequencies stems from the experimental noise, which usually affects the impedance more at low frequencies.