Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study - dataset

Abstract:from [1]

Polymer nanocomposites (PNCs) have shown great potential to meet the ever-growing requirements of modern engineering applications. Nowadays, molecular dynamics (MD) simulations are increasingly employed to complement experimental work and thereby gain a deeper understanding of the complex structure–property relations of PNCs. However, with respect to the thermoplastic’s mechanical behavior, the role of its average molar mass is rarely addressed, and many MD studies only consider uniform (monodispersed) polymers. Therefore, this contribution investigates the impact that and the dispersity Đ have on the stiffness and strength of PNCs through coarse-grained MD. To this end, we employed a Kremer–Grest bead–spring model and observed the expected increase in the mechanical performance of the neat polymer for larger . Our results indicated that the unimodal molar mass distribution does not impact the mechanical behavior in the investigated dispersity range Đ. For the PNC, we obtained the same -dependence and Đ-independence of the mechanical properties over a wide range of filler sizes and contents. This contribution proves that even simple MD models can reproduce the experimentally well researched effect of the molar mass. Hence, this work is an important step in understanding the complex structure–property relations of PNCs, which is essential to unlock their full potential.

Contact:

Maximilian RiesInstitute of Applied MechanicsFriedrich-Alexander-Universität Erlangen-NürnbergEgerlandstr. 591058 Erlangen

Software:

All MD simulations were performed with LAMMPS [2,3], version: 23 Oct 2022 / 20220623

Compiled withCompiler: GNU C++ 11.2.0 with OpenMP not enabledC++ standard: C++11

Active compile time flags:-DLAMMPS_GZIP-DLAMMPS_SMALLBIG

Installed packages:CLASS2 DPD-BASIC EXTRA-DUMP INTEL KSPACE MANYBODY MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT PERI

Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]

Post-processing Matlab R2019b

License:

Creative Commons Attribution 4.0 International

Context:

Data set supplementing  journal paper:

[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.

Content:

structure of data set:

    -01_neat     containing the neat polymer simulations        -01_uniform        containing samples with uniform chain lengths        -02_distributed        containing samples with distributed chain lengths            -100-dist            samples with mean molar mass 100            -200-dist            samples with mean molar mass 200    -02_PNC    containing the polymer nanocomposite simulations        -01_uniform        containing samples with uniform chain lengths            -T_0.2            simulations at temperature 0.2            -T_0.3            simulations at temperature 0.3            -T_0.4            simulations at temperature 0.4        -02_distributed        containing samples with distributed chain lengths            -T_0.2            simulations at temperature 0.2            -T_0.3            simulations at temperature 0.3            -T_0.4            simulations at temperature 0.4    

naming convention for simulation folders

    - neat polymer simulations        example: GTP_UT_num_chains-80_num_beads_per_chain-500-8        * num_chains: number of polymer chains        * num_beads_per_chain: molar mass (chain length)        * distribution: standard deviation of gauss distribution govering dispersity        * "trailing number": batch number of sample        - polymer nanocomposite simulations        example: GTP_rF-5_nF-10_chainlen-5_7-T_0.2        * rF: nanofiller radius        * nF: number of nanofillers        * chainlen: molar mass (chain length)

Each simulation directory contains:

    lammps input file (*.in) of the specific simulation

    data file (*.data) containing the initial sample configuration

    input.prm: input parameters of the specific simulation (read by the input file)

    meta.info: meta data of the specific simulation run

    LAMMPS_out:    simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below

        thermo_out.Dat: raw output 

        thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)

        thermo_out_STD.Dat: standard deviation of raw output

Output quantities (columns of *.Dat files):Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].

    Step: time step 

    Time: time 

    TotEng: total energy 

    PotEng: potential energy

    KinEng: kinetic energy 

    E_pair: pair energy 

    E_bond: bond energy 

    E_angle: angle energy 

    E_dihed: dihedral energy 

    Temp: temperature

    Press: hydrostatic pressure

    Pxx: xx component of pressure tensor 

    Pyy: yy component of pressure tensor 

    Pzz: zz component of pressure tensor 

    Pxy: xy component of pressure tensor

    Pxz: xz component of pressure tensor

    Pyz: yz component of pressure tensor

    Volume: volume of simulation box 

    Lx: box length in x direction  

    Ly: box length in y direction  

    Lz: box length in z direction  

    Density: density  

    c_RG: radius of gyration scalar 

    c_RG[1]: squared radius of gyration tensor (xx component)  

    c_RG[2]: squared radius of gyration tensor (yy component)  

    c_RG[3]: squared radius of gyration tensor (zz component)  

    c_RG[4]: squared radius of gyration tensor (xy component)  

    c_RG[5]: squared radius of gyration tensor (xz component)  

    c_RG[6]: squared radius of gyration tensor (yz component)  

    c_bondave[1]: bond energy averaged over all atoms  

    c_bondave[2]: bond distance averaged over all atoms  

    c_bondave[3]: squared bond distance averaged over all atoms  

    c_angleave[1]: angle energy averaged over all atoms  

    c_angleave[2]: angle averaged over all atoms degree

    c_angleave[3]: cosine of angle 

    c_angleave[4]: squared cosine of angle 

    c_MSD[1]: mean squared displacement x-direction  

    c_MSD[2]: mean squared displacement y-direction  

    c_MSD[3]: mean squared displacement z-direction  

    c_MSD[4]: total mean squared displacement  

    c_COM[1]: x coordinate of center of mass  

    c_COM[2]: y coordinate of center of mass  

    c_COM[3]: z coordinate of center of mass  

    v_strain_xx: xx component of engineering strain tensor   

    v_strain_yy: yy component of engineering strain tensor    

    v_strain_zz: zz component of engineering strain tensor    

    v_vMisesequivstress: von Mises equivalent stress 

    v_Cauchy_xx: xx component of stress tensor  

    v_Cauchy_yy: yy component of stress tensor

    v_Cauchy_zz: zz component of stress tensor

    v_Cauchy_xy: xy component of stress tensor 

    v_Cauchy_xz: xz component of stress tensor 

    v_Cauchy_yz: yz component of stress tensor 

    v_strain_xy: xy component of engineering strain tensor   

    v_strain_xz: xz component of engineering strain tensor   

    v_strain_yz: yz component of engineering strain tensor   

References:

[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.

[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.

[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022.

[4] J. Roksvaag, M.Ries . “A fast self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, manuscript in preparation