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Understanding the phase behavior and dynamics of multi-component polymeric systems is essential for designing materials used in applications ranging from biopharmaceuticals to consumer products. While computational tools for understanding the equilibrium properties of such systems are relatively mature, simulation platforms for investigating non-equilibrium behavior are comparatively less developed. Dynamic self-consistent field theory (DSCFT) is a method that retains essential microscopic thermodynamics while enabling a continuum-level understanding of multi-component, multi-phase diffusive transport. A challenge with DSCFT is its high computational complexity and cost, along with the difficulty of incorporating thermal fluctuations. External potential dynamics (EPD) offers a more efficient approach to studying inhomogeneous polymers out of equilibrium, providing similar accuracy to DSCFT but with significantly lower computational cost. In this work, we introduce an extension of EPD to enable efficient and stable simulations of multi-species, multi-component polymer systems while embedding thermodynamically consistent noise. We validate this framework through simulations of a triblock copolymer melt and spinodally decomposing binary and ternary polymer blends, demonstrating its capability to capture key features of phase separation and domain growth. Furthermore, we highlight the role of thermal fluctuations in early stage coarsening. This study provides new insights into the interplay between stochastic and deterministic effects in the dynamic evolution of polymeric fluids, with the EPD framework offering a robust and scalable approach for investigating the complex dynamics of multi-component polymeric materials. 

Field-theoretic simulations are numerical methods for polymer field theory, which include fluctuation corrections beyond the mean-field level, successfully capturing various mesoscopic phenomena. Most field-theoretic simulations of polymeric fluids use the auxiliary field (AF) theory framework, which employs Hubbard–Stratonovich transformations for the particle-to-field conversion. Nonetheless, the Hubbard–Stratonovich transformation imposes significant limitations on the functional form of the non-bonded potentials. Removing this restriction on the non-bonded potentials will enable studies of a wide range of systems that require multi-body or more complex potentials. An alternative representation is the hybrid density-explicit auxiliary field theory (DE-AF), which retains both a density field and a conjugate auxiliary field for each species. While the DE-AF representation is not new, density-explicit field-theoretic simulations have yet to be developed. A major challenge is preserving the real and non-negative nature of the density field during stochastic evolution. To address this, we introduce positivity-preserving schemes that enable the first stable and efficient density-explicit field-theoretic simulations (DE-AF FTS). By applying the new method to a simple fluid, we find thermodynamically correct results at high densities, but the algorithm fails in the dilute regime. Nonetheless, DE-AF FTS is shown to be broadly applicable to dense fluid systems including a simple fluid with a three-body non-bonded potential, a homopolymer solution, and a diblock copolymer melt. 

Field-theoretic simulations are numerical treatments of polymer field theory models that go beyond the mean-field self-consistent field theory level and have successfully captured a range of mesoscopic phenomena. Inherent in molecularly-based field theories is a “sign problem” associated with complex-valued Hamiltonian functionals. One route to field-theoretic simulations utilizes the complex Langevin (CL) method to importance sample complex-valued field configurations to bypass the sign problem. Although CL is exact in principle, it can be difficult to stabilize in strongly fluctuating systems. An alternate approach for blends or block copolymers with two segment species is to make a “partial saddle point approximation” (PSPA) in which the stiff pressure-like field is constrained to its mean-field value, eliminating the sign problem in the remaining field theory, allowing for traditional (real) sampling methods. The consequences of the PSPA are relatively unknown, and direct comparisons between the two methods are limited. Here, we quantitatively compare thermodynamic observables, order-disorder transitions, and periodic domain sizes predicted by the two approaches for a weakly compressible model of AB diblock copolymers. Using Gaussian fluctuation analysis, we validate our simulation observations, finding that the PSPA incorrectly captures trends in fluctuation corrections to certain thermodynamic observables, microdomain spacing, and location of order-disorder transitions. For incompressible models with contact interactions, we find similar discrepancies between the predictions of CL and PSPA, but these can be minimized by regularization procedures such as Morse calibration. These findings mandate caution in applying the PSPA to broader classes of soft-matter models and systems.