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11 results for “coarse-grained model”

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zenodo44/100

Supplementary material for "Song et al., Modelling Simul. Mater. Sci. Eng., 2021: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies"

This zip archive contains supplementary material in the form of datasets and jupyter notebooks that are used in the following publication: <ul> <li>authors: Hengxu Song, Nina Gunkelmann, Giacomo Po, and Stefan Sandfeld</li> <li>journal: Modelling Simul. Mater. Sci. Eng.</li> <li>year: 2021</li> <li>title: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies</li> </ul>

opencc-by-4.0Dec 2020View details →

zenodo40/100

Protein secondary-structure description with a coarse-grained model: code and datasets in ActivePapers format

This file contains the supplementary material for the publication Protein secondary-structure description with a coarse-grained model by Gerald R. Kneller and K. Hinsen http://dx.doi.org/10.1107/S1399004715007191 Acta Cryst. (2015). D71, 1411-1422 Datasets in this file 1) ScrewFit and ScrewFrame parameters for ideal secondary-structure elements    Scripts:      /code/import_ideal_structures      /code/analyze_ideal_structures 1.1) The PDB files generated with Chimera    /data/ideal_structures/3-10.pdb    /data/ideal_structures/alpha.pdb    /data/ideal_structures/beta-antiparallel.pdb    /data/ideal_structures/beta-parallel.pdb    /data/ideal_structures/pi.pdb 1.2) The corresponding MOSAIC datasets    /data/ideal_structures/3-10    /data/ideal_structures/alpha    /data/ideal_structures/beta-antiparallel    /data/ideal_structures/beta-parallel    /data/ideal_structures/pi 1.3) The ScrewFit parameters    /data/ideal_structures/screwfit/3-10    /data/ideal_structures/screwfit/alpha    /data/ideal_structures/screwfit/beta-antiparallel    /data/ideal_structures/screwfit/beta-parallel    /data/ideal_structures/screwfit/pi 1.4) The ScrewFrame parameters    /data/ideal_structures/screwframe/3-10    /data/ideal_structures/screwframe/alpha    /data/ideal_structures/screwframe/beta-antiparallel    /data/ideal_structures/screwframe/beta-parallel    /data/ideal_structures/screwframe/pi 2) Statistics for ScrewFit and ScrewFrame parameters computed    for the ASTRAL SCOPe subset with less than 40% sequence identity.    Scripts:      /code/astral_analysis      /code/fit_rho_distributions      /code/plot_histograms 2.1) The ASTRAL database (link to published ActivePaper)    /data/astral_2.04 2.2) The histograms for the ScrewFit and ScrewFrame parameters      for the all-alpha and all-beta subsets    /data/histograms/astral_alpha/screwfit    /data/histograms/astral_alpha/screwframe    /data/histograms/astral_beta/screwfit    /data/histograms/astral_beta/screwframe 2.3) The Gaussians fitted to the peaks in the distributions for rho    /data/fitted_rho_distributions/screwfit    /data/fitted_rho_distributions/screwframe 2.4) Plots    /documentation/delta.pdf    /documentation/delta_q.pdf    /documentation/delta_r.pdf    /documentation/p.pdf    /documentation/rho-detail.pdf    /documentation/rho.pdf    /documentation/sigma.pdf    /documentation/tau.pdf 3) Comparison of secondary-structure identification between ScrewFrame    and DSSP.    Script:      /code/compare_secondary_structure_assignments      /code/plot_histograms 3.1) The histograms of the lengths of secondary-structure elements    /data/histograms/secondary_structure/length-alpha-dssp    /data/histograms/secondary_structure/length-alpha-screwframe    /data/histograms/secondary_structure/length-beta-dssp    /data/histograms/secondary_structure/length-beta-screwframe 3.2) The 2D histograms of the number of residues inside identified      secondary-structure elements    /data/histograms/secondary_structure/n-alpha    /data/histograms/secondary_structure/n-beta 3.3) The distribution of rho inside alpha helices    /data/histograms/secondary_structure/rho-alpha-dssp 3.3) Plots    /documentation/lengths-alpha.pdf    /documentation/lengths-beta.pdf    /documentation/n-alpha.pdf    /documentation/n-beta.pdf    /documentation/rho-alpha-dssp.pdf 4) Illustration for myoglobin and VADC-1    Scripts:      /code/import_myoglobin_vdac      /code/analyze_myoglobin      /code/analyze_vdac      /code/perturbation_analysis 4.1) Imported structures in MOSAIC format:      PDB code 1A6G for myoglobin      PDB code 2K4T for VDAC-1    /data/myoglobin    /data/VDAC-1 4.2) Plots showing rho and delta    /documentation/rho-myoglobin.pdf    /documentation/delta-myoglobin.pdf 4.3) Tube models for visualization with Chimera    /documentation/myoglobin-tube.bld    /documentation/VDAC-1-tube.bld 4.4) Sensitivity to perturbations in the coordinates    /documentation/rho-perturbed-myoglobin.pdf    /documentation/delta-perturbed-VDAC-1.pdf    /documentation/rho-perturbed-myoglobin.pdf    /documentation/delta-perturbed-VDAC-1.pdf    /documentation/myoglobin-perturbation.pdf    /documentation/VDAC-1-perturbation.pdf 5) Analysis of CA-only structures in the PDB    Scripts:      /code/ca_analysis      /code/import_calpha_structures      /code/plot_histograms 5.1) Imported CA-only structures in MOSAIC format    /data/pdb_ca_only_structures 5.2) Histograms for ScrewFrame parameters    /data/histograms/ca_only_structures 5.3) Plots    /documentation/delta_ca.pdf    /documentation/delta_q_ca.pdf    /documentation/delta_r_ca.pdf    /documentation/p_ca.pdf    /documentation/rho_ca.pdf    /documentation/sigma_ca.pdf    /documentation/tau_ca.pdf

opencc-zeroJul 2015View details →

zenodo40/100

Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset

Abstract: (from [1]) <blockquote> The addition of nano-sized filler particles enhances the mechanical performance of polymers. The resulting properties of the polymer nanocomposite depend on a complex interplay of influence factors such as material pairing, filler size, and content as well as filler-matrix adhesion. As a complement to experimental studies, numerical methods, such as molecular dynamics (MD), facilitate an isolated examination of the individual factors in order to understand their interaction better. However, particle-based simulations are, in general, computationally very expensive, rendering a thorough investigation of nanocomposites’ mechanical behavior both expensive and time-consuming. Therefore, this paper presents a fast coarse-grained MD model for a generic nanoparticle-reinforced thermoplastic. First, we examine the matrix and filler phase individually, which exhibit isotropic elasto-viscoplastic and anisotropic elastic behavior, respectively. Based on this, we demonstrate that the effect of filler size, filler content, and filler-matrix adhesion on the stiffness and strength of the nanocomposite corresponds very well with experimental findings in the literature. Consequently, the presented computationally efficient MD model enables the analysis of a generic polymer nanocomposite. In addition to the obtained insights into the mechanical behavior, the material characterization provides the basis for a future continuum mechanical description, which bridges the gap to the engineering scale.  </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All MD simulations were performed with LAMMPS [2], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [3] Post-processing Matlab R2019b Evaluation of polymer entanglements with Z1-Algorithm [4]   License: Creative Commons Attribution 4.0 International   Context: Data set supplementing  journal paper: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. This dataset contains the results presented in [1] and the necessary data to obtain those as well as supplementary information. Content: supplementary material: supplementary_information.pdf data:     folder names vary depending on the context, explained in the following:   01_matrix <ul> <li> 01_equilibration sample equilibration to different temperatures nomenclature: equil_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>[-<batch_ID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> <li> batch_ID: 2-5  </li> </ul> </li> <li> 02_temperature_dependence uniaxial tension simulations to identify temperature dependence nomenclature: 01_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> </ul> </li> <li> 03_directional_dependence uniaxial tension simulations to prove isotropy in Y and Z direction; X direction in 04_rate_dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-5 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 04_rate_dependence uniaxial tension simulations to identify strain rate dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>[-<batchID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 05_cyclic_loading sinusoidal uniaxial deformation nomenclature: 05_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 06_relaxation relaxation subsequent to time-proportional deformation nomenclature: 07_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude>_relax <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 07_simple_shear time-proportional simple shear deformation with different strain rates nomenclature: SS_P2VPSi-rate_<strain_rate>-<batchID> <ul> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 08_large_deformation uniaxial deformation up to 100% strain nomenclature: 02_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperatur>-strain_<max_strain> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> max_strain: 1 </li> </ul> </li> </ul> 02_filler <ul> <li> 01_Silica_equilibration sample equilibration </li> <li> 02_time_proportional time-proportional uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>-strain_<max_strain>-rate_<strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> max_strain: 0.1 </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> </ul> </li> <li> 03_time_periodic time-periodic uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>_sin-ampl_<strain_amplitude>-rate_<max_strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_amplitude: 0.025 </li> </ul> </li> </ul> 03_composite <ul> <li> 01_equilibration sample equilibration nomenclature: equil_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 02_uniaxial-tension uniaxial tension simulations nomenclature: UT_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 03_filler-maxtrix-adhesion equilibration and uniaxial deformation of samples with mid and weak filler-matrix adhesion (for strong adhesion see 01_equilibration and 02_uniaxial-tension nomenclature: see above </li> <li> 04_IP_equilibration equilibration of samples to evaluate the microstructure for neat polymer and composites with filler radius 2.5-7.5 nomenclature: P2VPSi-<chains>x<chain_atoms>_rNP_<filler_radius>-nNP_<filler_number>_pos_<filler_pos>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> filler_radius: 0 (neat), 2.5, 5.0, 7.5 </li> <li> filler_number: 0 (neat), 1 </li> <li> batchID: 1-20 </li> </ul> </li> </ul>       Each simulation directory contains: <ul> <li> lammps input file (*.in) of the specific simulation </li> <li> data file (*.data) containing the initial sample configuration </li> <li> input.prm: input parameters of the specific simulation (read by the input file) </li> <li> meta.info: meta data of the specific simulation run </li> <li> LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li> thermo_out.Dat: raw output  </li> <li> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter) </li> <li> thermo_out_STD.Dat: standard deviation of raw output </li> </ul> </li> </ul>   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]. <ul> <li> Step: time step  </li> <li> Time: time  </li> <li> TotEng: total energy  </li> <li> PotEng: potential energy </li> <li> KinEng: kinetic energy  </li> <li> E_pair: pair energy  </li> <li> E_bond: bond energy  </li> <li> E_angle: angle energy  </li> <li> E_dihed: dihedral energy  </li> <li> Temp: temperature </li> <li> Press: hydrostatic pressure </li> <li> Pxx: xx component of pressure tensor  </li> <li> Pyy: yy component of pressure tensor  </li> <li> Pzz: zz component of pressure tensor  </li> <li> Pxy: xy component of pressure tensor </li> <li> Pxz: xz component of pressure tensor </li> <li> Pyz: yz component of pressure tensor </li> <li> Volume: volume of simulation box  </li> <li> Lx: box length in x direction   </li> <li> Ly: box length in y direction   </li> <li> Lz: box length in z direction   </li> <li> Density: density   </li> <li> c_RG: radius of gyration scalar  </li> <li> c_RG[1]: squared radius of gyration tensor (xx component)   </li> <li> c_RG[2]: squared radius of gyration tensor (yy component)   </li> <li> c_RG[3]: squared radius of gyration tensor (zz component)   </li> <li> c_RG[4]: squared radius of gyration tensor (xy component)   </li> <li> c_RG[5]: squared radius of gyration tensor (xz component)   </li> <li> c_RG[6]: squared radius of gyration tensor (yz component)   </li> <li> c_bondave[1]: bond energy averaged over all atoms   </li> <li> c_bondave[2]: bond distance averaged over all atoms   </li> <li> c_bondave[3]: squared bond distance averaged over all atoms   </li> <li> c_angleave[1]: angle energy averaged over all atoms   </li> <li> c_angleave[2]: angle averaged over all atoms degree </li> <li> c_angleave[3]: cosine of angle  </li> <li> c_angleave[4]: squared cosine of angle  </li> <li> c_MSD[1]: mean squared displacement x-direction   </li> <li> c_MSD[2]: mean squared displacement y-direction   </li> <li> c_MSD[3]: mean squared displacement z-direction   </li> <li> c_MSD[4]: total mean squared displacement   </li> <li> c_COM[1]: x coordinate of center of mass   </li> <li> c_COM[2]: y coordinate of center of mass   </li> <li> c_COM[3]: z coordinate of center of mass   </li> <li> v_strain_xx: xx component of engineering strain tensor    </li> <li> v_strain_yy: yy component of engineering strain tensor     </li> <li> v_strain_zz: zz component of engineering strain tensor     </li> <li> v_vMisesequivstress: von Mises equivalent stress  </li> <li> v_Cauchy_xx: xx component of stress tensor   </li> <li> v_Cauchy_yy: yy component of stress tensor </li> <li> v_Cauchy_zz: zz component of stress tensor </li> <li> v_Cauchy_xy: xy component of stress tensor  </li> <li> v_Cauchy_xz: xz component of stress tensor  </li> <li> v_Cauchy_yz: yz component of stress tensor  </li> <li> v_strain_xy: xy component of engineering strain tensor    </li> <li> v_strain_xz: xz component of engineering strain tensor    </li> <li> v_strain_yz: yz component of engineering strain tensor    </li> </ul>   References: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. [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] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, Zenodo, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a>

opencc-by-4.0Sep 2022View details →

zenodo40/100

Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set

Abstract: from [1] <blockquote> Polymer nanocomposites are an important class of materials for engineering applications due to their high versatility and good mechanical properties combined with low density. By directly attaching the polymer chains to the nanofillers, the so-called grafting, a better load transfer between matrix and filler is achieved, and, in addition, a better dispersion of the fillers is obtained. Both result in enhanced mechanical properties. Since experimental investigations on the nanoscale are extremely challenging, complementary numerical studies are needed to unravel the mechanical behavior of polymer nanocomposites. To this end, molecular dynamics is ideally suited since it captures the microstructure, but is also numerically expensive. Therefore, this contribution presents a fast coarse-grained molecular dynamics model for the investigation of the mechanical behavior of grafted polymer nanocomposites. For this purpose, we extend an existing model by grafting bonds, which allows us to compare the effect of untreated and grafted fillers directly. In particular, we investigate the influence of filler content, grafting degree, and filler size on the stiffness and strength of the polymer (grafted) nanocomposites. We conclude that the grafting bonds have little effect on the stiffness, while the strength is significantly improved compared to the untreated fillers, which is in agreement with the literature. The presented molecular dynamics model for polymer grafted nanocomposites provides the basis for further investigations, particularly of the crucial matrix-filler interphase. In addition, this contribution translates molecular dynamics insights into mechanical properties, which bridges the gap to the engineering scale and thus represents a step towards exploiting the full potential of polymer (grafted) nanocomposites. </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD 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, S. Reber, P. Steinmann, & S. Pfaller, “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” Forces in Mechanics, vol. 12, p. 100 207, 2023. Content: structure of data set: <ul> <li>04_Equilibration folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content variation of filler content</li> <li>02_grafting_density variation of grafting density</li> <li>03_grafting_potential variation of grafting potential</li> <li>04_filler_size variation of filler size</li> <li>05_reference reference samples without grafting</li> </ul> </li> <li>05_UT folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content variation of filler content</li> <li>02_grafting_density variation of grafting density</li> <li>03_grafting_potential variation of grafting potential</li> <li>04_filler_size variation of filler size</li> <li>05_reference reference samples without grafting</li> </ul> </li> </ul> Each simulation directory contains: <ul> <li> lammps input file (*.in) of the specific simulation </li> <li> data file (*.data) containing the initial sample configuration </li> <li> input.prm: input parameters of the specific simulation (read by the input file) </li> <li> meta.info: meta data of the specific simulation run </li> <li> LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li> thermo_out.Dat: raw output  </li> <li> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter) </li> <li> thermo_out_STD.Dat: standard deviation of raw output </li> </ul> </li> </ul> 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]. <ul> <li> Step: time step  </li> <li> Time: time  </li> <li> TotEng: total energy  </li> <li> PotEng: potential energy </li> <li> KinEng: kinetic energy  </li> <li> E_pair: pair energy  </li> <li> E_bond: bond energy  </li> <li> E_angle: angle energy  </li> <li> E_dihed: dihedral energy  </li> <li> Temp: temperature </li> <li> Press: hydrostatic pressure </li> <li> Pxx: xx component of pressure tensor  </li> <li> Pyy: yy component of pressure tensor  </li> <li> Pzz: zz component of pressure tensor  </li> <li> Pxy: xy component of pressure tensor </li> <li> Pxz: xz component of pressure tensor </li> <li> Pyz: yz component of pressure tensor </li> <li> Volume: volume of simulation box  </li> <li> Lx: box length in x direction   </li> <li> Ly: box length in y direction   </li> <li> Lz: box length in z direction   </li> <li> Density: density   </li> <li> c_RG: radius of gyration scalar  </li> <li> c_RG[1]: squared radius of gyration tensor (xx component)   </li> <li> c_RG[2]: squared radius of gyration tensor (yy component)   </li> <li> c_RG[3]: squared radius of gyration tensor (zz component)   </li> <li> c_RG[4]: squared radius of gyration tensor (xy component)   </li> <li> c_RG[5]: squared radius of gyration tensor (xz component)   </li> <li> c_RG[6]: squared radius of gyration tensor (yz component)   </li> <li> c_bondave[1]: bond energy averaged over all atoms   </li> <li> c_bondave[2]: bond distance averaged over all atoms   </li> <li> c_bondave[3]: squared bond distance averaged over all atoms   </li> <li> c_angleave[1]: angle energy averaged over all atoms   </li> <li> c_angleave[2]: angle averaged over all atoms degree </li> <li> c_angleave[3]: cosine of angle  </li> <li> c_angleave[4]: squared cosine of angle  </li> <li> c_MSD[1]: mean squared displacement x-direction   </li> <li> c_MSD[2]: mean squared displacement y-direction   </li> <li> c_MSD[3]: mean squared displacement z-direction   </li> <li> c_MSD[4]: total mean squared displacement   </li> <li> c_COM[1]: x coordinate of center of mass   </li> <li> c_COM[2]: y coordinate of center of mass   </li> <li> c_COM[3]: z coordinate of center of mass   </li> <li> v_strain_xx: xx component of engineering strain tensor    </li> <li> v_strain_yy: yy component of engineering strain tensor     </li> <li> v_strain_zz: zz component of engineering strain tensor     </li> <li> v_vMisesequivstress: von Mises equivalent stress  </li> <li> v_Cauchy_xx: xx component of stress tensor   </li> <li> v_Cauchy_yy: yy component of stress tensor </li> <li> v_Cauchy_zz: zz component of stress tensor </li> <li> v_Cauchy_xy: xy component of stress tensor  </li> <li> v_Cauchy_xz: xz component of stress tensor  </li> <li> v_Cauchy_yz: yz component of stress tensor  </li> <li> v_strain_xy: xy component of engineering strain tensor    </li> <li> v_strain_xz: xz component of engineering strain tensor    </li> <li> v_strain_yz: yz component of engineering strain tensor    </li> </ul> References: [1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” Forces in Mechanics, vol. 12, p. 100 207, 2023. [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] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, Zenodo, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a>

opencc-by-4.0Sep 2023View details →

zenodo36/100

A bottom-up coarse-grained model for interactions of lipids with TiO2 nanoparticles

Supplemetary information data to the paper: M.Ivanov and A.P.Lyubartsev, "Development of a bottom-up coarse-grained model for interactions of lipids with TiO  nanoparticles", J. Comput. Chem.m 2024. Doi: <a href="https://doi.org/10.1002/jcc.27310">10.1002/jcc.27310</a>

opencc-by-4.0Apr 2024View details →

zenodo36/100

Resolution limit of data-driven coarse-grained models spanning chemical space

This repository contains all databases referenced in the supporting information of the paper titled "Resolution limit of data-driven coarse-grained models spanning chemical space" by Kiran H. Kanekal and Tristan Bereau.

opencc-by-4.0Jul 2019View details →

zenodo36/100

Comparative performance of coarse-grained IDP models at different resolutions

Files for simulations. Molsim start scripts and final outputs, MARTINI Stark start scripts (including force field files, and some intermediate data). Not all intermediate data available, but all data necessary to reproduce simulations should be included. MARTINI simulations follow the procedure "Dry energy minimization" -> "Solvation" -> "Salting" -> "Energy minimization" -> "NVT equilibration" -> "NPT equilibration" -> "Production Run", where the output from the previous step is the input into the next step. For some folders, the output from previous step has been copied, but not always. In case of another simulation, but using a bigger box, some files from the simulation with smaller box were re-used. Thus, if a file seems to be missing, check the folder with corresponding simulation using a smaller box. Some "analysis files" are included, e.g. the radius of gyration for each time step, in order to allow for the reproduction of graphs in upcoming publication, and further exploration.

opencc-by-4.0Jan 2023View details →

zenodo32/100

Coarse-grain Models of HIV-1 Nucleoids

These are PDB-format files that may be read by most molecular graphics programs. The script JSmol.script will create a customized view with appropriate radii, using JSmol. The PDB format is modified in the following ways: --coordinates are in nanometers --RNA strands have atom name "P" and residue name "RNA" --integrase subunits have atom name "CA" and residue name "IN" --nucleocapsid subunits have atom name "N", and residue names   "NCO" for experimentally-observed positions and   "NCR" for randomly-placed positions Each file contains 25 instances of the nucleoid model. Each model includes 2 gRNA strands, 2000 nucleocapsid subunits, and 140/0 integrase subunits. Nine models are included in this release, exploring two types of variables: 1) Three types of starting models:     "selfavoidingGag" assumes that the RNA forms a non-overlapping random walk on      the lattice of gag proteins in the immature virion    "overlappingGag" assumes a similar random walk, but the RNA strands may overlap    "random" assumes that RNA is released from gag and randomly fills the interior of      the virion before condensation  2) Three assumptions about integrase interaction:    35 integrase tetramers, 70 integrase dimers, and no integrase More information is available at: http://ccsb.scripps.edu/latticenucleoid/HIVnucleoid

opencc-by-4.0May 2019View details →

zenodo32/100

Dataset for "Constructing many-body dissipative particle dynamics models of fluids from bottom-up coarse-graining"

<ul> <li>The trajectory files used for processing time correlation functions, as reported in the original paper, are provided here.</li> <li>The analysis tools can be found in the following GitHub repository: <a href="https://github.com/jaehyeokjin/ManyBodyDPD/tree/main/Time-Correlation" target="_new" rel="noopener">ManyBodyDPD/Time-Correlation</a>. These tools are designed to work with the two trajectory files included in this repository.</li> </ul>

opencc-by-4.0Sep 2024View details →

zenodo24/100

Arena: Rapid and accurate reconstruction of full-atomic RNA structures from coarse-grained models

Arena-main.zip contains the source code, which is also available on GitHub. benchmarking_datasets.zip contains the datasets used for benchmarking Arena and the other RNA reconstruction programs: pdb (original files), pdb_input (original files with missing atoms added by Arena), pdb_input_C3_prime (input files with only the C3' atoms), pdb_input_glycoN (input files with only the glycosidic N1/N9 atoms), pdb_input_P (input files with only the P atoms), pdb_input_P_C1_base (input files with only the P, C1', and base atoms), pdb_input_P_ribose (input files with only the backbone atoms), NAST (files from NAST structure prediction), and SimRNA (files from SimRNA structure prediction). fasta.zip contains the nucleotide sequences of the RNAs in the benchmarking dataset, which is a required input for Rosetta rna_thread. lists.zip contains text files of the PDB IDs used for benchmarking. RNA_classes.zip contains tsv files of the benchmarking dataset split by type of RNA.

opencc-by-4.0Jan 2023View details →

zenodo20/100

Coarse-grained moment from atmospheric model

Data from high-resolution models.

openJan 2023View details →

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Last verified 2026-04-29Open record