(310j) An Information-Theoretic Approach for Probing Macromolecular Phase Separation Via Data Compression

A recurring challenge in molecular simulations is the selection of appropriate order parameters to describe complex molecular systems. An entropy-based descriptor derived directly from molecular coordinates would be especially valuable for studying assembly and aggregation processes. Recent work has introduced data compression as a novel approach to quantify order in both equilibrium and non-equilibrium systems and both identify and characterize phase transitions. While this has been applied toward both discrete and continuous particle models, as well as experimental data, the potential of this data compression approach for characterizing complex macromolecular systems remains unexplored. This work presents an initial application of the data compression methodology to identify and characterize phase transitions from molecular simulation trajectories of polymeric systems. Furthermore, we explore how this approach may be further applied toward the study of folding transitions in biomolecular systems and potentially manipulated to explore entropy as an order parameter when modeling complex molecular assemblies.