To effectively identify the global phase emergence behaviors, we employ NMF deconvolution to the PL datasets with four optimal components (Figure ). Here we implemented Gaussian processing (GP) regression to compensate for the uncertainty caused by the large compositional steps (8%), which effectively interpolates between the NMF components in the loading maps . In both top and bottom surfaces, the emergence maps of n=1, n=2-3, n≥4 2D, and 3D-like HP phases in the ternary compositional space are visualized. We focus on the phase emergence behaviors of the 3D-like HPs (i.e., Phase Set 4 in Figure ), which shows high probabilities at the lower right corner in the ternary phase diagram -<15% BDAPbI4, 15-40% PEA2PbI4, and 60-70% CsPbI3 -for both the top and bottom surfaces. That is, the compositions within this range are anticipated to provide 3D-like HP phases while the vertical phase inhomogeneities are greatly mitigated. Specifically, we manually select three compositions -CsPbI3:PEA2PbI4:BDAPbI4 of 76:16:8, 68:24:8, and 60:32:8 -as optimal coordinates in the ternary diagram, which is likely suitable for PV applications. Indeed, the ternary quasi-2D HPs film with a 60:32:8 composition ratio exhibits a homogenized, and δ-CsPbI3-free surface, as seen in hyperspectral CL characterization (Figure ), validating the effectiveness of NMF-based prediction.