Data for: Efficient dynamical field-theoretic simulations for multi-component systems
Data files
Mar 24, 2025 version files 1.62 GB
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Dryad.tar.gz
1.62 GB
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README.md
3.22 KB
Abstract
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.
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), 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
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Omega is the structure metric in the paper
- phiA is the volume fraction of A
SFig_4
1. data_phi.csv
- t is time
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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
All data collected here are computational results or analyzed quantities careful review of the manuscript is suggested prior to utilizing the dataset.