DOI: 10.5061/dryad.m0cfxppcp
title: Data for: Efficient dynamical field-theoretic simulations for multi-component systems

description:     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.
description: All data collected here are computational results or analyzed
 quantities careful review of the manuscript is suggested prior to
 utilizing the dataset.
description: # Data for: Efficient dynamical field-theoretic simulations for
 multi-component systems
 [https://doi.org/10.5061/dryad.m0cfxppcp](https://doi.org/10.5061/dryad.m0cfxppcp) ## Description of the data and file structure No experiments were conducted, and all results are from numerical computations. Visualization Toolkit (.vtk) files reporting density can be viewed with Paraview ([www.paraview.org](https://www.paraview.org)), an open-source and free post-processing visualization engine. ### Files and variables Fig_1 1\. dscft_data_17.csv * x refers to to the length in Rg * phi1 is the volume fraction of A * phi2 is the volume fraction of B * phi3 is the volume fraction of C 2\. dscft_data_28.csv * x refers to to the length in Rg * phi1 is the volume fraction of A * phi2 is the volume fraction of B * phi3 is the volume fraction of C 3\. epd_data_17.csv * x refers to to the length in Rg * phi1 is the volume fraction of A * phi2 is the volume fraction of B * phi3 is the volume fraction of C 4\. epd_data_28.csv * x refers to to the length in Rg * phi1 is the volume fraction of A * phi2 is the volume fraction of B * phi3 is the volume fraction of C 5\. DSCFT_average.csv * t is the time * phi_A is the average maximum value of A * std_error is the standard error of the mean Fig_2 Includes vtk files that can be visualized using paraview also correspond to videos. 1\. DSCFT_binary 2\. EPD_binary 3\. SEPD_binary Fig_3 1\. A.csv * Corresponds to the top plot * t is time * D is the domain size in Rg * Derr is the standard error of the mean * method is either EPD or DSCFT 2\. B.csv * Corresponds to the bottom plot * t is timed * D is the domain size in Rg * Derr is the standard error of the mean * C is the chain concentration Fig_4 1\. A.csv \* Corresponds to the top plot \* t is time \* Omega is the structure metric in the paper \* Amp, tstar and beta are fits also in the paper 2\. B.csv \* C is Chain Concentration \* tstar and beta are fitting parameters Fig_5 Includes vtk files that can be visualized using paraview also correspond to videos. 1\. phi_A_0.5 2\. phi_A_0.05 3\. phi_A_0.33 Fig_6 1\. A_0.5.csv * phiA_0.5 * t is time * D is the Domain size in Rg 2\. A_0.05.csv * phiA_0.05 * t is time * D is the Domain size in Rg 3\. A_0.33333.csv * phiA_0.33333 * t is time * D is the Domain size in Rg 4\. B.csv * t is time * D is Domain size in Rg * C is the chain concentration Fig_7 Includes vtk files that can be visualized using paraview also correspond to videos. 1\. 3D_phiA_0.5 2\. 3D_phiA_0.05 SFig_1 1\. AF_S{ii}.csv * k in Rg^-1 * S(k) * ii refers to components 2\. EPD_S{ii}.csv * k in Rg^-1 * S(k) * ii refers to components SFig_2 1\. Fitted_Parameters.csv * C is Chain Concentration * tstar and beta are fitting parameters SFig_3 1\. data_C.csv * t is time * Omega is the structure metric in the paper * C is the chain concentration 2\. data_phi.csv * t is time * Omega is the structure metric in the paper - phiA is the volume fraction of A SFig_4 1\. data_phi.csv * t is time * Omega is the structure metric in the paper - phiA is the volume fraction of A 2\. Domain.csv * t is time * D is Domain size in Rg