This observation constitutes the primary finding of this study, demonstrating that despite optical intensity signals approaching the detection limit, unsupervised ML methods can reliably identify regions of preferential pitting. These regions would otherwise be identifiable only through resource-intensive and timeconsuming laboratory analyses employing high-precision instrumentation (e.g., STM, ToF-SIMS, AFM). At this stage, the conclusion remains purely phenomenological and calls for a physically grounded explanation, which will be provided in the next section.