To better understand the global trends in the spacer-dependent phase emergence behaviors across compositional space, we utilize non-negative matrix factorization (NMF), an unsupervised machine learning (ML) approach. This multivariate data analysis has been successfully implemented to explore the large chemical space in the quasi-2D HPs, which effectively identifies key features associated with complex materials systems . Through NMF deconvolutions, time-evolutions of key PL features -reflecting the corresponding HP phases -and their composition dependencies (both at the top and bottom surfaces) are identified. This allows us to visualize the qualitative phase diagrams in each combinatorial library. Figure and S10 reveal the key phase emergence behaviors at the top surface of each combinatorial library. Here, we used three components (four for DPA-based system) as an optimal number for NMF analysis, which can reasonably deconvolute the PL datasets.