Construction of phase diagram for binary polymer blend with unsupervised machine learning

Obtaining the phase diagram of complex fluid mixtures such as molecular liquid mixtures, polymer coacervates, and polymer blends is a challenging computational task. This is because traditional computational methods require the insertion of molecules, which is difficult for large molecules. In this work, we obtain the phase diagram of complex fluids using unsupervised machine learning (ML) methods. We show that construction of a feature vector is a crucial aspect to the success of ML methods. Using the ``affinity-based" feature, which assigns a "spin-like" variable to each atom, and the autoencoder we calculate the phase diagram of several model compounds. We show that the method is accurate for the phase diagram of simple binary mixtures and polymer blends, when compared to conventional methods. We also use the method to obtain the phase diagram of polymers in ionic liquids and address recent controversies. UML and local affinity feature vector open the new way to study phase behaviors of complex fluids without performing special simulations.