The conception of the degradation chamber in this work aims at high-throughput, machineinterpretable degradation data that can be used to run automated searches for more stable materials. To avoid false positives when exploring unknown compositional search spacesespecially with low level of human intervention, as is often the case in high-throughput experimentsthe authors recommend clustering the optical data retrospectively for targeted in-depth analysis. Here, we demonstrate the clustering approach for the perovskite film samples of varying MA-FA-Cs lead iodide compositions from Sun and Tiihonen . We cluster the optical data without composition information and target the XRD analysis to the compositional and optical centroids of the clusters. The comparison illustrates that the clusters with differing color change behaviors have compositional and phase differences already as fresh samples, thus strengthening the color proxy.