Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture

Phase separation in mixed lipid systems has been extensively studied both experimentally and theoretically because of its biological importance. A detailed description of such complex systems undoubtedly requires novel mathematical frameworks that are capable of decomposing and categorizing the evolution of thousands if not millions of lipids involved in the phenomenon. The interpretation and analysis of molecular dynamics (MD) simulations representing temporal and spatial changes in such systems are still a challenging task. Here, we present an unsupervised machine learning approach based on nonnegative matrix factorization called NMFk that successfully extracts latent (i.e., not directly observable) features from the second layer neighborhood profiles derived from coarse-grained MD simulations of a ternary lipid mixture. Our results demonstrate that NMFk extracts physically meaningful features that uniquely describe the phase separation such as locations and roles of different lipid types, formation of nanodomains, and timescales of lipid segregation.