Phase Behavior of Poly(ethylene oxide) in Room Temperature Ionic Liquids: A Molecular Simulation and Deep Neural Network Study

The
phase behavior of polymers in room temperature ionic liquids
is a topic of considerable interest. In this work we study the phase
diagram of poly­(ethylene oxide) in four imidazolium ionic liquids
(ILs) using molecular simulation. We develop united atom models for
1-butyl-2,3-dimethyl­imidazolium ([BMMIM]), 1-ethyl-2,3-dimethyl­imidazolium
([EMMIM]), and 1-ethyl-3-methyl­imidazolium ([EMIM]) in an analogous
fashion to previously developed models for 1-butyl-3-methyl­imidazolium
([BMIM]) and tetrafluoroborate ([BF4]) using symmetry-adapted
perturbation theory. At high temperatures we obtain the coexistence
concentrations using an interface method where the polymer and IL
are simulated in a large elongated box, and an interface between coexisting
phases is formed. At lower temperatures we use a deep neural network
(DNN) method. The input descriptors for the DNN are the cohesive energy
of mixing, the volume change of mixing, and the coordination numbers
between cation and polymer, all of which are obtained from simulations
of mixed systems at a series of temperatures. The DNN is trained by
using the phase-separated systems at high temperatures and a mixed
phase at low temperatures. The method predicts a lower critical solution
temperature which decreases as the alkyl chain length on the cation
is decreased, consistent with experiment. The simulations show that
methylation of the cation has little effect on the phase diagram.
This is in contrast to what is seen in experiments but could be because
the polymer chains in the simulations are too short. At low temperatures
the chains display two conformational motifs, namely a crown ether
conformation and a ring conformation, each of which can wrap the chain
around a single cation. This provides the entropic penalty for mixing
and a reason for demixing as the temperature is raised. Such conformations
might not be possible for longer chains. The combination of data-driven
techniques and molecular simulation shows promise in the study of
the phase behavior and physical properties of complex fluids.